Information processor, information processor control method, and program

Abstract

To make it possible to estimate a type of a recording medium with high accuracy.SOLUTION: An information processor comprises: acquisition means which acquires a characteristic value of a recording medium to which a paper is fed; a recording medium group representing a plurality of predetermined recording media on the basis of the characteristic value acquired by the acquisition means; first determination means which determines the type of the paper-fed recording medium or the recording medium group from among the plurality of recording media and different recording media; and second determination means which determines the type of the paper-fed recording medium from among the recording medium group when a result in the first determination means indicates the recording medium group. When it is determined by the first determination means that the type of the paper-fed recording medium is one, the second determination means is not used for determination of the paper-fed recording medium.SELECTED DRAWING: Figure 5

Description

[Technical field]

[0001] The present disclosure relates to a technique for estimating the type of a recording medium. [Background technology]

[0002] In the commercial and industrial printing market, the uses of output are diverse, including CAD line drawings, posters, artwork, and signage. For this reason, a wide variety of recording media with different characteristics are used to suit these uses. As the number of types of recording media increases, the task of selecting the type of recording media fed into a recording device becomes more complicated. In recent years, recording devices are being equipped with functions that improve usability by automatically identifying the type of recording media fed into the device.

[0003] Patent Document 1 discloses a method for determining the type of recording medium from information on the characteristics of a fed recording medium using a trained model based on machine learning. Examples of information on the characteristics of the recording medium include reflectance, thickness, and basis weight. The trained model is trained using training data that associates information on the characteristics of the recording medium with the type of recording medium. Therefore, the type of recording medium can be obtained by acquiring information on the characteristics of the fed recording medium and inputting it into the trained model. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2022-58434 A Summary of the Invention [Problem to be solved by the invention]

[0005] In the method described in Patent Document 1, a single trained model is used to distinguish all recording media, so there is a risk of misclassifying recording media with similar characteristics.

[0006] An object of the present disclosure is to provide an information processing device that can estimate the type of a recording medium with high accuracy. [Means for solving the problem]

[0007] An information processing device according to one aspect of the present disclosure comprises an acquisition means for acquiring characteristic values ​​of the recording medium to be fed, a first determination means for determining the type or group of recording media to be fed from among a predetermined group of recording media representing a plurality of recording media and a recording medium different from the plurality of recording media based on the characteristic values ​​acquired by the acquisition means, and a second determination means for determining the type of recording medium to be fed from among the group of recording media if the result of the first determination means indicates that it is the group of recording media, and is characterized in that if the first determination means determines that there is one type of recording medium to be fed, the second determination means is not used to determine the recording medium to be fed. Effect of the Invention

[0008] According to the present disclosure, the type of recording medium can be estimated with high accuracy. [Brief description of the drawings]

[0009] [Figure 1] FIG. 1 is a perspective view showing an example of a recording apparatus. [Diagram 2] FIG. 2 is a cross-sectional view showing an example of a main part of a recording apparatus. [Diagram 3] FIG. 2 is a block diagram showing an example of the configuration of a control system of the printing apparatus. [Figure 4] FIG. 2 is a cross-sectional view showing an example of the periphery of a media sensor. [Diagram 5] 10 is a flowchart illustrating an example of a sheet type estimation process. [Figure 6] FIG. 4 is a diagram showing an example of a sheet characteristic. [Figure 7] FIG. 13 is a diagram illustrating an example of a sheet type estimation table. [Figure 8] FIG. 2 is a block diagram showing an example of the configuration of a control system of the printing apparatus. [Figure 9] FIG. 13 is a diagram illustrating an example of a sheet type estimation table. [Figure 10] Schematic diagram of a DNN. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] Preferred embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings. Note that the following embodiments do not limit the subject matter of the present disclosure, and not all combinations of features described in the following embodiments are necessarily essential to the solutions of the present disclosure. Note that the same configurations will be described with the same reference numerals. Furthermore, the relative arrangements, shapes, etc. of the components described in the embodiments are merely examples, and are not intended to limit the scope of the present disclosure to only those.

[0011] In the following description of the embodiment, "recording" includes not only the formation of significant information such as characters and figures, but also the formation of images, designs, patterns, etc. on a sheet. In the present embodiment, a roll sheet is assumed as the sheet, but cut paper, cloth, plastic film, etc. may also be used. Furthermore, "ink" should be broadly interpreted and refers to a liquid that can be applied to a sheet to form images, designs, patterns, etc., or to process the sheet, or to be used for ink processing.

[0012] <<First embodiment>> <Inkjet recording device> FIG. 1 is a perspective view showing an example of a recording device 101 that performs industrial / commercial printing in this embodiment. FIG. 2 is a cross-sectional view showing an example of a main part of the recording device 101. FIG. 3 is a block diagram showing an example of a control configuration of the recording device 101. The configuration of the recording device 101 will be described below with reference to FIG. 1 to FIG. 3.

[0013] In this embodiment, the information processing device is not limited to the recording device 101. A server (for example, a cloud server) capable of exchanging various types of data with the recording device 101 may also be used as the information processing device.

[0014] Hereinafter, the transport direction in which the sheet S is transported in the printing apparatus 101 is defined as the +Y direction. The direction in which the print head 204 ejects ink onto the sheet S is defined as the -Z direction. The directions in which the print head 204 is movable are defined as the ±X directions.

[0015] The recording device 101 rotatably holds a roll sheet R in which a sheet S is wound in a roll shape. The roll sheet R is rotated by a roll drive motor, and the sheet S is supplied from the roll sheet R to a conveying roller 203. The conveying roller 203 can rotate while holding the sheet S. The conveying roller drive motor rotates the conveying roller 203, and the sheet S is conveyed to a position where the recording head 204 can perform recording. The recording head 204 is mounted on a carriage (not shown) and configured to move back and forth in the X direction. The recording head 204 records an image by ejecting ink onto the conveyed sheet S while moving in the X direction. The sheet S on which the image has been recorded is discharged from a discharge section downstream of the recording head 204 in the conveying direction, and is loaded onto the basket 103.

[0016] The operation panel 102 is an interface module that accepts various operations from the user. The user can perform various settings for the recording device 101 using various switches or a touch panel provided on the operation panel 102. The various settings for the recording device 101 include, for example, settings for the size and type of the sheet S. The operation panel 102 also displays estimation results, which will be described later.

[0017] In the conveying direction, a sheet detection sensor 202 is disposed upstream of the conveying roller 203. When the sheet detection sensor 202 detects that the sheet S has been supplied from the rolled sheet R, the conveying operation of the sheet S is started. The conveying operation of the sheet S is performed by synchronizing the roll drive motor and the conveying roller drive motor. At this time, the recording apparatus 101 can estimate the type of the sheet S by estimating the sheet type, which will be described later. Details will be described later.

[0018] In the conveying direction, a media sensor 206 and an ultrasonic transmitter 207 are disposed upstream of the sheet detection sensor 202. The media sensor 206 is disposed above the sheet S in the direction of gravity (Z direction), and the ultrasonic transmitter 207 is disposed below the sheet S in the direction of gravity. The media sensor 206 and the ultrasonic transmitter 207 are used to estimate the sheet type, which will be described later.

[0019] An image is recorded on the sheet S as follows. First, the recording device 101 executes a conveying operation to convey the sheet S to a position facing the recording head 204. Next, a recording operation is executed to scan the recording head 204 in a cross direction intersecting (orthogonal to) the conveying direction of the sheet S while discharging ink, thereby recording an image in an area of ​​the sheet S corresponding to the recording head 204. Next, after conveying the sheet S by a predetermined distance, ink is discharged while scanning the recording head 204 in the cross direction. In this way, a desired image is recorded on the sheet S by alternately executing the conveying operation of the sheet S and the image recording operation. The sheet S on which the image has been recorded is sequentially conveyed downstream of the recording head 204 in the conveying direction. The conveyed sheet S is cut by a cutter 205 provided in a discharge section. The cut sheet S is loaded into a basket 103.

[0020] 3 is a block diagram showing an example of the configuration of a control system included in the recording apparatus 101. The recording apparatus 101 has an operation panel 102, a recording head 204, a CPU 301, a sensor control unit 302, and an input / output IF (Interface) 303. The recording apparatus 101 has a USB port 304, a memory 305, a motor control unit 306, and a RAM 320. The recording apparatus 101 also has a sheet detection sensor 202, a media sensor 206, an ultrasonic transmitter 207, and a carriage encoder 307. The recording apparatus 101 also has a roll drive motor 308, a conveyor roller drive motor 309, a carriage drive motor 310, a lift drive motor 311, a cutter drive motor 312, and a media sensor lift motor 313. The memory 305 has a program 351.

[0021] The motor control unit 306 controls each driving motor according to a program 351 stored in the memory 305. The roll driving motor 308 rotates the spool 201 to convey the sheet S from the roll sheet R in the conveying direction. The conveying roller driving motor 309 rotates the conveying roller 203 to convey the sheet S to a position facing the recording head 204. The conveying roller driving motor 309 is provided with an encoder for detecting the conveyance amount of the sheet S. The conveyance amount of the sheet S can be detected by measuring the rotation amount of the encoder. The carriage driving motor 310 can move a carriage (not shown) and the recording head 204 mounted thereon by rotating a carriage belt (not shown). The lift driving motor 311 moves the carriage and the recording head 204 up and down. The cutter driving motor 312 drives the cutter. The media sensor lifting motor 313 lifts and lowers the media sensor 206.

[0022] Various setting information and the like are input to CPU 301 via input / output IF 303 by user operations from operation panel 102 or a PC connected to USB port 304. The input information is stored in memory 305. CPU 301 can read out the information stored in memory 305 as needed and perform various processes on the read out information. That is, CPU 301 includes a processing unit that executes various processes.

[0023] The CPU 301 controls the carriage encoder 307, the sheet detection sensor 202, the media sensor 206, and the ultrasonic transmitter 207 via the sensor control unit 302, and obtains output data output from each unit. The CPU 301 executes various controls based on inputs from the carriage encoder 307, the sheet detection sensor 202, and the media sensor 206. The CPU 301 controls the carriage encoder 307, the sheet detection sensor 202, the media sensor 206, and the ultrasonic transmitter 207 via the sensor control unit 302, and obtains information. The CPU 301 also executes various controls based on inputs from the carriage encoder 307, the sheet detection sensor 202, and the media sensor 206. The RAM 320 is used as a temporary work area.

[0024] <Estimation of seat type> The process of estimating the type of sheet S executed by the CPU 301 in this embodiment will be described with reference to Figs. 4 to 7. Fig. 4 is a cross-sectional view showing an example of the periphery of the media sensor 206. Fig. 5 is a flow chart showing an example of the process of estimating the type of sheet S from paper feed. Fig. 6 is an example of data obtained by detecting the characteristics of the sheet S using the media sensor 206 and the ultrasonic transmitter 207, respectively. Fig. 7 is an estimation table of the type of sheet S in this embodiment, showing the range of an index y (described later) corresponding to the type of sheet S. In this embodiment, the types of sheets S to be estimated are nine types, from recording medium 1 to recording medium 9. Furthermore, the characteristics of recording medium 6 and recording medium 7 are similar. Similarly, the characteristics of recording medium 8 and recording medium 9 are similar.

[0025] The process of the flowchart shown in FIG. 5 is realized by the CPU 301 of the recording device 101 reading out the program 351 stored in the memory 305 or the like into the RAM 320 and executing it. Note that some or all of the functions of the steps in FIG. 5 may be realized by hardware such as an ASIC or an electronic circuit. The symbol "S" in the explanation of each process indicates a step in the flowchart. The process shown in FIG. 5 is started, for example, when the user sets the rolled sheet S in the recording device 101. Alternatively, the process may be started when it is detected that the user has set the rolled sheet S in the recording device 101 and then input a predetermined operation by the user on the operation panel 102.

[0026] In S501, the CPU 301 feeds the sheet S. First, the CPU 301 detects that the user has set the rolled sheet R in the recording device 101. Then, the CPU 301 rotates the rolled sheet R by the roll drive motor 308. As a result, the sheet S is supplied from the rolled sheet R to the conveying rollers 203. Then, the sheet detection sensor 202 disposed upstream of the conveying rollers 203 detects that the sheet S has reached the conveying rollers 203. When the sheet detection sensor 202 detects that the sheet S has reached the conveying rollers 203, the CPU 301 stops driving the roll drive motor 308. At the position where the sheet S is detected by the sheet detection sensor 202, the sheet S is conveyed to a position facing the media sensor 206 and the ultrasonic transmitter 207. After that, the CPU 301 proceeds to the process of S502.

[0027] In S502, the CPU 301 performs sensing. That is, the CPU 301 measures the characteristics of the sheet S by controlling the media sensor 206 and the ultrasonic transmitter 207 via the sensor control unit 302. As shown in FIG. 4, the media sensor 206 is composed of a CIS (Contact Image Sensor) 401 and a microphone 402. A roller 403 is disposed opposite the CIS 401. The ultrasonic transmitter 207 is disposed opposite the microphone 402. The media sensor 206 is raised and lowered by the media sensor lift motor 313, so that the sheet S can be sandwiched between the CIS 401 and the roller 403. By sandwiching the sheet S, the characteristics of the sheet S can be stably measured. The CPU 301 reads the surface image of the sheet S with the CIS 401 while conveying the sheet S again in a state where the sheet S is sandwiched between the CIS 401 and the roller 403. Then, when the sensing is completed, the CPU 301 separates the media sensor 206 from the sheet S by raising the media sensor 206 using the media sensor lift motor 313.

[0028] 6(a) and (b) show examples of surface images of sheet S acquired using the CIS 401. Fig. 6(c) and (d) show examples of electrical signals of ultrasonic waves transmitted through sheet S acquired using the ultrasonic transmitter 207 and microphone 402.

[0029] The CIS 401 is a line sensor extending in the width direction of the sheet S, and acquires one-dimensional (one line) image data. The CPU 301 acquires image data of the sheet S using the CIS 401 while synchronously driving the roll drive motor 308 and the conveying roller drive motor 309 in a state where the sheet S is sandwiched between the CIS 401 and the roller 403. By reading the image of the sheet S using the CIS 401 while conveying the sheet S in this way, two-dimensional image data as shown in Figs. 6(a) and (b) can be acquired. Fig. 6(a) is an example of a surface image of Japanese paper, and Fig. 6(b) is an example of a surface image of Yupo paper. In Figs. 6(a) and (b), the CIS direction corresponds to the width of the CIS 401 (width in the X direction intersecting the sheet S), and the conveying direction corresponds to the conveying amount of the sheet S measured by the CIS 401. Note that, although an example of measurement using a one-dimensional sensor as the CIS 401 is shown here, the surface image of the sheet S may be measured using a two-dimensional sensor. Also, when the CIS 401 is measuring the surface image, the ultrasonic transmitter 207 and the microphone 402 (sound pickup sensor) obtain ultrasonic electrical signals as shown in Figs. 6(c) and (d). Fig. 6(c) is an example of an ultrasonic electrical signal transmitted through Japanese paper, and Fig. 6(d) is an example of an ultrasonic electrical signal transmitted through Yupo paper. In this embodiment, an example of obtaining an ultrasonic electrical signal in conjunction with the measurement of the surface image by the CIS 401 will be described, but this is not limiting. The measurement of the surface image and the acquisition of the ultrasonic electrical signal may be performed separately. Also, the ultrasonic electrical signal may be obtained without conveying the sheet S. Thereafter, the CPU 301 proceeds to the process of S503.

[0030] In S503, the CPU 301 derives characteristic values. That is, the CPU 301 derives a feature amount related to the surface information and a feature amount related to the cross-sectional information of the sheet S from the characteristics of the sheet S measured in S502 described above, using a feature amount derivation method previously stored in the memory 305. That is, the CPU 301 derives a feature amount related to the surface information and a feature amount related to the cross-sectional information of the sheet S from the surface image of the sheet S and the electrical signal of the ultrasonic waves.

[0031] The CPU 301 derives three feature amounts related to the surface information of the sheet S from the surface image of the sheet S as shown in FIGS. 6(a) and (b). The first feature amount is brightness. The brightness is derived as the average value of all pixel values. The second feature amount is unevenness in the CIS direction. The unevenness in the CIS direction is derived as the average value of the absolute values ​​of the differences between adjacent pixel values ​​in the CIS direction. The third feature amount is unevenness in the transport direction. The unevenness in the transport direction is derived as the average value of the absolute values ​​of the differences between adjacent pixel values ​​in the transport direction.

[0032] The CPU 301 derives three feature amounts relating to the cross-sectional information of the sheet S from the electrical signal of the ultrasonic waves transmitted through the sheet S as shown in FIGS. 6(c) and (d). The first feature amount is peak 1. Peak 1 is derived as the maximum voltage value in the period from time t1 to time t2. The second feature amount is peak 2. Peak 2 is derived as the maximum voltage value in the period from time t2 to time t3. The third feature amount is peak 3. Peak 3 is derived as the maximum voltage value in the period from time t3 to time t4. Note that although the maximum voltage value is described here, it may also be the minimum voltage value. The cross-sectional information of the sheet S corresponds to information such as the thickness and basis weight of the sheet.

[0033] In this manner, in S503, the CPU 301 can derive six feature amounts relating to the surface information and cross-sectional information of the sheet S from the characteristics of the sheet S measured in S502.

[0034] In this embodiment, Japanese paper and Yupo paper are taken as examples. An example of the relationship between the feature quantities of the two is shown below. The brightness is higher for Yupo paper than for Japanese paper. The unevenness in the CIS direction is higher for Japanese paper than for Yupo paper. The unevenness in the transport direction is higher for Japanese paper than for Yupo paper. For example, a sheet S with a whiter color and flatter surface has a higher brightness. Also, the greater the unevenness of the surface, the greater the unevenness in both the CIS direction and the transport direction. Note that some types of sheets have longitudinal or transverse grains, and only one of the two is more uneven. The thicker the sheet is, the smaller the peak value of the ultrasonic electrical signal becomes. Japanese paper is thicker than Yupo paper. For this reason, the peak value of Yupo paper is larger than that of Japanese paper. Also, even with the same thickness, the peak value changes depending on the cross section (the material or density constituting the sheet). Specifically, the peak value tends to be smaller when a material (medium) with a larger acoustic impedance is used. Thereafter, the CPU 301 proceeds to the process of S504.

[0035] In S504, the CPU 301 roughly classifies the type of the sheet S. That is, the CPU 301 estimates the type of the sheet S from the six feature amounts derived in S503 described above, using an estimation table shown in FIG. 7 that is stored in advance in the memory 305. Note that, although an example in which the estimation table is stored in the memory 305 will be described in this embodiment, the estimation table may also be stored outside the recording device 101. In order to estimate the type of the sheet S, the CPU 301 derives an index y shown in the following formula (1).

[0036]

number

[0037] The index y is the sum of the weighted values ​​of each feature amount, and is a value for estimating the type of the sheet S. f1 to f6 are six feature amounts derived in S503. Specifically, f1 to f6 are feature amounts corresponding to the brightness, the unevenness in the CIS direction, the unevenness in the conveying direction, peak 1, peak 2, and peak 3, respectively. w1 to w6 are coefficients for weighting each feature amount. The weighting coefficients w1 to w6 are determined so that as many types as possible of the types of the sheet S to be estimated, namely, the recording medium 1 to the recording medium 9, can be estimated with high accuracy. Therefore, the CPU 301 may not be able to estimate types of the sheet S having similar characteristics (for example, the recording medium 6 and the recording medium 7, or the recording medium 8 and the recording medium 9) with high accuracy.

[0038] The estimation table in FIG. 7 defines the minimum and maximum values ​​of the index y range corresponding to the recording medium 1 to the recording medium 9. These data are obtained by deriving the minimum and maximum values ​​of the index y corresponding to the recording medium 1 to the recording medium 9 from each measurement data obtained by repeatedly measuring the characteristics of the recording medium 1 to the recording medium 9. In addition, the estimation table in FIG. 7 shows not only the range of the index y corresponding to the rough classification in S504, but also the range of the index y corresponding to the detailed classification 1 in S507 described later and the detailed classification 2 in S508 described later. In this embodiment, the type of the sheet refers to the name of the sheet, such as "plain paper", "glossy paper", "semi-glossy paper", and "glossy film". However, the type of the sheet is not limited to this. For example, the state of the recording medium may be the type of the sheet, such as a sheet having a certain unevenness and basis weight being recording medium 1 and a sheet having another unevenness and basis weight being recording medium 2.

[0039] 7, in S504, when the index y derived from the feature amount of the sheet S is in the range of 6.2 to 6.9, the CPU 301 estimates the type of the sheet S as recording medium 1. Similarly, when the index y is in the range of 4.9 to 5.3, the CPU 301 estimates the type as recording medium 2, when the index y is in the range of 3.4 to 3.6, when the index y is in the range of 15.3 to 16.0, the CPU 301 estimates the type as recording medium 4, when the index y is in the range of 7.0 to 7.5, the CPU 301 estimates the type as recording medium 6, when the index y is in the range of 8.5 to 9.0, the CPU 301 estimates the type as recording medium 7, when the index y is in the range of 8.5 to 8.9, the CPU 301 estimates the type as recording medium 8, when the index y is in the range of 9.9 to 10.6, the CPU 301 estimates the type as recording medium 9.

[0040] When the index y is included in the range of a plurality of types of sheets S, the CPU 301 derives the center value between the minimum and maximum values ​​of each range, and estimates the type of sheet S of the recording medium 1 to 9, which has the closest center value to the index y. For example, when the index y is 8.6, the index is included in the range of recording medium 6 and recording medium 7. The center value between the minimum and maximum values ​​of recording medium 6 is 8.76. The center value between the minimum and maximum values ​​of recording medium 7 is 8.69. Therefore, when the index y is 8.6, the CPU 301 estimates the recording medium 7 as the type of sheet S. On the other hand, when the index y is not included in the range of any type of sheet S, the CPU 301 derives the center value between the minimum and maximum values ​​of each range, and estimates the type of sheet S of the recording medium 1 to 9, which has the closest center value to the index y. For example, when the index y is 1.0, the index is not included in any range of recording medium 1 to 9. Of the center values ​​between the minimum and maximum values ​​of recording medium 1 to recording medium 9, the center value between the minimum and maximum values ​​of recording medium 3 (3.50) is the closest to the index y. Therefore, when index y is 1.0, CPU 301 estimates that recording medium 3 is the type of sheet S. Then, CPU 301 proceeds to the process of S505.

[0041] In this way, in S504, the CPU 301 can determine the type of the sheet S by the estimation table shown in FIG. 7. However, for the recording medium 6 and the recording medium 7, which have similar characteristics, and the recording medium 8 and the recording medium 9, the ranges of the indices shown in the estimation table overlap, so there is a high possibility of erroneous estimation. For example, in the estimation table of FIG. 7, the ranges of the indices of the recording medium 6 and the recording medium 7 overlap from 8.5 to 8.9. In other words, when the index y is in the range of 8.5 to 8.9, there is a risk of erroneous estimation by the method of estimating the type of the sheet S from the center value between the minimum and maximum values ​​of the ranges of the indices of the recording medium 6 and the recording medium 7 described above. In this case, in order to perform detailed classification described later and estimate the type of the sheet S with high accuracy, the estimation results of S504 are the recording medium 6 and the recording medium 7. Then, the recording medium 6 and the recording medium 7 are collectively classified as the recording medium group 1. Similarly, when the index y is in the range of 9.9 to 10.0, the estimation results of S504 are the recording medium 8 and the recording medium 9. Then, the recording medium 8 and the recording medium 9 are collectively classified as the recording medium group 2. In this embodiment, when the index y is in an overlapping range among the index ranges of the multiple recording media shown in the estimation table in Fig. 7, detailed classification described below is performed, but is not limited to this. For example, when the index y is in the range of any of the indexes of the multiple recording media having overlapping index ranges, detailed classification may be performed on a recording medium group representing the multiple recording media.

[0042] In S505, CPU 301 determines whether to perform detailed classification 1 in S507 or detailed classification 2 in S508, which will be described later, based on the result of the estimation of the type of sheet S in S504 described above. If the estimation result in S504 is recording medium group 1 (recording medium 6 and recording medium 7), CPU 301 proceeds to a process of S507, which will be described later, and performs detailed classification 1. On the other hand, if the estimation result in S504 is recording medium group 2 (recording medium 8 and recording medium 9), CPU 301 proceeds to a process of S508, which will be described later, and performs detailed classification 2. If the estimation result in S504 is other than recording medium group 1 and recording medium group 2, CPU 301 proceeds to a process of S506, which will be described later, and displays the estimation result on operation panel 102.

[0043] In S507, the CPU 301 estimates the type of the sheet S from the six feature amounts derived in S503 described above, using the estimation table shown in Fig. 7, which is stored in advance in the memory 305. That is, in S507, the CPU 301 estimates the type of the sheet S from the range of the index y shown in detailed classification 1 of the estimation table in Fig. 7, using the same six feature amounts as those used in estimating the type of the sheet S in S504. In order to estimate the type of the sheet S, the CPU 301 derives the index y shown in Equation 1, as in S504 described above. However, the index y derived in S507 is the sum of values ​​obtained by weighting the six feature amounts derived in S503 with different weightings than those in S504. The weighting coefficients w1 to w6 are determined in order to estimate the recording medium 6 and the recording medium 7 with high accuracy.

[0044] As shown in the estimation table of FIG. 7, in S507, when the index y derived from the feature amount of the sheet S is in the range of 6.7 to 7.1, the CPU 301 estimates the type of the sheet S to be the recording medium 6. Similarly, when the index y is in the range of 5.1 to 5.4, the CPU 301 estimates the type of the sheet S to be the recording medium 7. Furthermore, when the index y is not included in the range of either the recording medium 6 or the recording medium 7, the CPU 301 derives the center value between the minimum and maximum values ​​of each range, and estimates the one whose center value is closest to the index y as the type of the sheet S. For example, when the index y is 9.2, it is not included in the range of either the recording medium 6 or the recording medium 7. The center value between the minimum and maximum values ​​of the recording medium 6 is 6.88. The center value between the minimum and maximum values ​​of the recording medium 7 is 5.27. Therefore, when the index y is 9.2, the CPU 301 estimates the type of the sheet S to be the recording medium 6. In this way, in S507, the CPU 301 can determine with high accuracy whether the type of the sheet S is the recording medium 6 or the recording medium 7. After that, the CPU 301 proceeds to the process of S506.

[0045] In S508, the CPU 301 estimates the type of the sheet S from the six feature amounts derived in the above-mentioned S503, using the estimation table shown in Fig. 7, which is stored in advance in the memory 305. That is, in S508, the CPU 301 estimates the type of the sheet S from the range of the index y shown in the detailed classification 2 of the estimation table in Fig. 7, using the same six feature amounts as those used in estimating the type of the sheet S in S504. The CPU 301 derives the index y shown in Equation 1 in order to estimate the type of the sheet S. However, the index y derived in S508 is the sum of values ​​obtained by weighting the six feature amounts derived in S503 differently from the above-mentioned S504 and S507. The weighting coefficients w1 to w6 are determined in order to estimate the recording medium 8 and the recording medium 9 with high accuracy.

[0046] As shown in the estimation table of FIG. 7, when the index y derived from the sheet S is in the range of 6.2 to 6.5 in S508, the CPU 301 estimates the type of the sheet S to be the recording medium 8. Similarly, when the index y is in the range of 5.8 to 6.1, the CPU 301 estimates the type of the sheet S to be the recording medium 9. Furthermore, when the index y is not included in the range of either the recording medium 8 or the recording medium 9, the CPU 301 derives the center value between the minimum and maximum values ​​of each range, and estimates the one whose center value is closest to the index y as the type of the sheet S. For example, when the index y is 4.5, it is not included in the range of either the recording medium 8 or the recording medium 9. The center value between the minimum and maximum values ​​of the recording medium 8 is 6.36. The center value between the minimum and maximum values ​​of the recording medium 9 is 5.98. Therefore, when the index y is 4.5, the CPU 301 estimates the type of the sheet S to be the recording medium 9. In this way, in S508, the CPU 301 can determine with high accuracy whether the type of the sheet S is the recording medium 8 or the recording medium 9. After that, the CPU 301 proceeds to the process of S506.

[0047] In S506, the CPU 301 presents the estimation (determination) result of the sheet S to the user by displaying it on the operation panel 102 via the input / output IF 303. If the detailed classification 1 of S507 described above has been executed, the estimation result of the sheet S will be the estimation result of the detailed classification 1. Similarly, if the detailed classification 2 of S508 described above has been executed, the estimation result of the detailed classification 2 will be. If the detailed classification 1 of S507 and the detailed classification 2 of S508 have not been executed, the estimation result of the sheet S will be the estimation result of the rough classification of S504 described above.

[0048] According to this embodiment, the type of the sheet S can be estimated with high accuracy by rough classification or a combination of rough classification and detailed classification.

[0049] <<Second embodiment>> In the first embodiment, the process of estimating the type of sheet S using the estimation table in Fig. 7 with the sum of values ​​weighted for each feature amount as an index has been described. In the present embodiment, the process of estimating the type of sheet S using a machine learning trained model will be described.

[0050] Fig. 8 is a block diagram showing an example of the control configuration of the recording device 101 in this embodiment. In Fig. 8, in addition to the program 351, the memory 305 includes a trained model 801 and an estimation table (not shown) shown in Fig. 9 described later. The other basic configurations are similar to the example described in the first embodiment. Note that the trained model 801 includes a trained model of coarse classification, a trained model of detailed classification 1, and a trained model of detailed classification 2 described later.

[0051] <Estimation of seat type> The operation of estimating the type of sheet S in this embodiment will be described with reference to FIG. 5 and FIG. 9. FIG. 5 is a flowchart showing an example of a process for estimating the type of sheet S. FIG. 9 is a diagram showing an example of an estimation table of sheet S in this embodiment. The estimation table in FIG. 9 defines the type of sheet S corresponding to the output value y of the trained model described later. As in the first embodiment, the types of sheet S to be estimated are nine types, from recording medium 1 to recording medium 9. In addition, the characteristics of recording medium 6 and recording medium 7 are similar. Similarly, the characteristics of recording medium 8 and recording medium 9 are similar. The difference from the first embodiment is four processes, S504, S505, S507, and S508, in the flowchart shown in FIG. 5 from paper feeding to estimating the type of sheet S and displaying it to the user. Therefore, in this embodiment, the above four processes will be mainly described.

[0052] In S504, the CPU 301 performs rough classification of the type of the sheet S. That is, the CPU 301 estimates the type of the sheet S from the six feature amounts derived in S503 using the trained model of rough classification stored in advance in the memory 305 and the estimation table shown in FIG. 9. In order to estimate the type of the sheet S, the CPU 301 acquires an output value y output from the trained model of rough classification. The trained model of rough classification receives the six feature amounts related to the sheet S derived in S503, and outputs the probability of the type of the sheet S to be estimated as an array for each type of the sheet S. Each element of the output array is linked to the type of the sheet S. That is, the index of each element of the output array is linked to the type of each sheet. In this embodiment, the index of the element with the maximum probability in the output array is set as the output value y of the trained model of rough classification. Then, the type of the sheet S linked to the output value y becomes the estimation result.

[0053] As shown in the estimation table of FIG. 9, in S504, when the output value y acquired from the feature amount of the sheet S using a learned model of rough classification described later is 0, the CPU 301 estimates the type of the sheet S as recording medium 1. Similarly, when the output value y is 1, the CPU 301 estimates the type of the sheet S as recording medium 2, when the output value y is 2, as recording medium 3, when the output value y is 3, as recording medium 4, and when the output value y is 4, as recording medium 5. When the output value y is 5, the estimation result of the type of the sheet S is recording medium 6 and recording medium 7. In this case, the recording medium 6 and the recording medium 7 are collectively classified as recording medium group 1. Similarly, when the output value y is 6, the estimation result of the type of the sheet S is recording medium 8 and recording medium 9. In this case, the recording medium 8 and the recording medium 9 are collectively classified as recording medium group 2. Thereafter, the CPU 301 proceeds to the process of S505.

[0054] As a result, in S504, when the output value y output from the learned model for rough classification is 0 to 4, the CPU 301 can determine the type of sheet S by dividing it into recording medium 1 to recording medium 5, respectively. On the other hand, when the output value y is 5, the CPU 301 estimates the type of sheet S to be recording medium 6 and recording medium 7. That is, in this case, the CPU 301 estimates that the type of sheet S will be classified into recording medium group 1 in the rough classification. Similarly, when the output value y is 6, the CPU 301 estimates that the type of sheet S will be classified into recording medium group 2 in the rough classification.

[0055] In S505, CPU 301 determines whether to execute detailed classification 1 in S507 or detailed classification 2 in S508, which will be described later, based on the result of the estimation of the type of sheet S in S504 described above. When the estimation result in S504 corresponds to recording medium group 1, CPU 301 determines to execute detailed classification 1 and proceeds to processing of S507. On the other hand, when the estimation result in S504 corresponds to recording medium group 2, CPU 301 determines to execute detailed classification 2 and proceeds to processing of S508. When the estimation result in S504 does not correspond to either recording medium group 1 or recording medium group 2, CPU 301 proceeds to processing of S506 and displays the estimation result on operation panel 102.

[0056] In S507, CPU 301 estimates the type of sheet S from the six feature amounts derived in S503 described above, using the trained model of detailed classification 1 stored in advance in memory 305 and the estimation table shown in Fig. 9. That is, in S507, CPU 301 estimates the type of sheet S using the same six feature amounts as those used in estimating the type of sheet S in S504. However, the output value y obtained in S507 is the output value of a trained model different from S504. To estimate the type of sheet S, CPU 301 obtains the output value y of the trained model, similar to S504 described above.

[0057] 9, in S507, when the output value y acquired from the feature amount of sheet S using the trained model of detailed classification 1 is 0, CPU 301 estimates that the type of sheet S is recording medium 6. Similarly, when the output value y is 1, CPU 301 estimates that the type of sheet S is recording medium 7. After that, CPU 301 proceeds to the process of S506.

[0058] As a result, in S507, the CPU 301 can determine the type of the sheet S, which was estimated as being in the recording medium group 1 in S504, to be the recording medium 6 or the recording medium 7.

[0059] In S508, CPU 301 estimates the type of sheet S from the six feature amounts derived in S503 described above, using the trained model of detailed classification 2 stored in advance in memory 305 and the estimation table shown in Fig. 9. That is, in S508, CPU 301 estimates the type of sheet S using the same six feature amounts as those used in estimating the type of sheet S in S504. However, the output value y obtained in S508 is the output value of a trained model different from S504 and S507. To estimate the type of sheet S, the output value y of the trained model of detailed classification 2 is obtained, as in S504 and S507 described above.

[0060] 9, in S508, when the output value y acquired from the feature amount of sheet S using a trained model of detailed classification 2 described later is 0, CPU 301 estimates that the type of sheet S is recording medium 8. Similarly, when the output value y is 1, CPU 301 estimates that the type of sheet S is recording medium 9. After that, CPU 301 proceeds to the process of S506.

[0061] As a result, in S508, the CPU 301 can determine that the type of the sheet S, which was estimated as being in the recording medium group 2 in S504, is the recording medium 8 or the recording medium 9.

[0062] According to this embodiment, the type of the sheet S can be estimated with high accuracy by rough classification or a combination of rough classification and detailed classification.

[0063] Depending on the type of sheet, the amount of ink used when printing an image with print head 204 and / or the amount of transport by the transport roller are changed. The type of sheet and these parameters are determined in advance and stored in memory 305. CPU 301 reads out the parameters stored in memory 305 that correspond to the type of sheet S estimated in the process of Fig. 5, and uses them for subsequent printing and transport processes. Note that if the user selects a sheet type different from the estimated result displayed in S506 as the type of sheet to be used for printing, the parameters corresponding to the type selected by the user are used for subsequent processes.

[0064] <Pre-trained model> 10 is a schematic diagram of a deep neural network (DNN). The above-mentioned three trained models 801 of this embodiment will be described with reference to FIG.

[0065] The trained model 801 of this embodiment is a DNN as shown in the schematic diagram of FIG. 9. The DNN receives data at an input layer 1001, propagates the data through an intermediate layer 1002, and outputs the data at an output layer 1003. Each layer has a plurality of nodes indicated by circles. The input data is propagated toward the output layer while being weighted and biased between the nodes of each layer. Adjusting parameters such as weighting and bias so that a specified output is obtained for a specified input is expressed as training a model. A trained model is called a trained model. A data set of input data and output data associated with the input data used to train a model is called teacher data.

[0066] In this embodiment, the input data of the teacher data are six feature amounts prepared for each type of sheet S. The six feature amounts are the same as the feature amounts derived in S503 described above, namely, brightness, unevenness in the CIS direction, unevenness in the transport direction, peak 1, peak 2, and peak 3. In this embodiment, the same feature amounts are used in the learning of all models. In addition, the input layer 1001 of the learned model has six nodes. Each feature amount is input to each node.

[0067] In this embodiment, the output data of the teacher data is an integer value indicating the type of the sheet S to be estimated. When there are two types of the sheet S to be estimated, the output data of the teacher data is 0 or 1. When there are nine types of the sheet S to be estimated, the output data of the teacher data is 0 to 8. When actually learning a model, an integer value converted into a one-hot vector is used. In this embodiment, a different combination of types of sheets S is used for each model to be learned. In addition, the output layer 1003 of the trained model has the same number of nodes as the types of sheets S to be estimated. Each node outputs the probability that the sheet S is each type of sheet. If the output of the trained model is regarded as an array, it can be considered that each element of the output array is the probability of each type of sheet. If each element of the output array is linked to each type of sheet, the index of each element is also linked to each type of sheet. In this embodiment, the estimation result of the trained model is the index of the element with the highest probability.

[0068] The first trained model is a trained model of rough classification used in S504 described above. The types of sheets S to be estimated are all recording media from recording medium 1 to recording medium 9. However, in consideration of the difficulty of estimating all recording media with high accuracy using a single trained model, types of sheets S with similar characteristics are grouped together in advance. In this embodiment, recording medium 6 and recording medium 7 are grouped together as recording medium group 1. Similarly, recording medium 8 and recording medium 9 are grouped together as recording medium group 2. The output data of the teacher data of rough classification is an integer value indicating the type of sheet S, with recording medium 1 being 0, recording medium 2 being 1, recording medium 3 being 2, recording medium 4 being 3, recording medium 5 being 4, recording medium group 1 being 5, and recording medium group 2 being 6.

[0069] The trained model trained using the above teacher data is called the trained model for rough classification. In the trained model for rough classification, as shown in FIG. 9, when feature quantities corresponding to recording medium 1 to recording medium 5 are input, 0 to 4 are output as the respective estimated results. When feature quantities corresponding to recording medium 6 or recording medium 7 are input, 5 is output as the estimated result. When feature quantities corresponding to recording medium 8 or recording medium 9 are input, 6 is output as the estimated result.

[0070] The second trained model is the trained model of detailed classification 1 used in S507 described above. The types of sheets S to be estimated are recording medium 6 and recording medium 7. The output data of the training data of detailed classification 1 is an integer value indicating the type of sheet S, with recording medium 6 being 0 and recording medium 7 being 1.

[0071] The trained model trained using the above training data is defined as the trained model of detailed classification 1. In the trained model of detailed classification 1, as shown in Fig. 9, when feature quantities corresponding to recording medium 6 and recording medium 7 are input, 0 or 1 is output as the respective estimation results.

[0072] The third trained model is the trained model of detailed classification 2 used in S508 described above. The types of sheets S to be estimated are recording medium 8 and recording medium 9. The output data of the training data of detailed classification 2 is an integer value indicating the type of sheet S, with recording medium 8 being 0 and recording medium 9 being 1.

[0073] The trained model trained using the above teaching data is defined as the trained model of detailed classification 2. In the trained model of detailed classification 2, as shown in Fig. 9, when feature quantities corresponding to recording medium 8 and recording medium 9 are input, 0 or 1 is output as the respective estimated results.

[0074] The above three trained models are generated by a machine learning device (not shown). The machine learning device is, for example, a PC. The machine learning device can record the trained model and a calculation method required for estimation using the trained model in memory 305 via USB port 304, input / output IF 303, and CPU 301. CPU 301 measures the characteristics of sheet S using media sensor 206 and ultrasonic transmitter 207, derives feature amounts related to surface information and cross-sectional information of sheet S from the measurement data, and inputs the derived feature amounts into the trained model to estimate the type of sheet S.

[0075] <<Other embodiments>> The above-described embodiments are merely examples of implementing the present disclosure, and are not intended to limit the present disclosure. For example, the present disclosure may be applied not only to a recording device that ejects ink onto a sheet to form an image, but also to a scanner that reads an image on a sheet, a post-processing machine that processes a sheet, or the like.

[0076] The estimation of the sheet type is not limited to being performed by the CPU 301 mounted on the recording apparatus 101, but may also be performed by a scanner, a post-processing device, a PC, or the like.

[0077] The number of detailed classifications (second estimation) shown in S507 and S508 is not limited to two, but may be one or more. For example, when the types of sheets S that are likely to be erroneously estimated in the rough classification (first estimation) of S504 are divided into five groups, five detailed classifications corresponding to each group may be provided. Furthermore, the number of types of recording media in the detailed classification corresponding to each group is not limited to two, but may be two or more. For example, when there are three types of sheets S that are likely to be erroneously estimated in the rough classification, and the three types can be accurately estimated in the detailed classification, the three types may be classified into one group in the rough classification.

[0078] The machine learning device may be mounted on the recording device 101. Also, the trained model may be generated in the recording device 101. Teacher data used when generating the trained model may be stored in the memory 305.

[0079] The above-mentioned feature amounts are not limited to six, and the color or thickness of the sheet S may be used. In the above-mentioned embodiment, the type of the sheet S is estimated from six feature amounts, but the present invention is not limited to this. For example, the type of the sheet S may be estimated from at least one of the feature amounts relating to the surface information of the sheet S and at least one of the feature amounts relating to the cross-sectional information of the sheet S. Furthermore, data acquired by the media sensor 206 or the like may be used as input data without deriving the feature amounts. The output data of the teacher data is not limited to the type of sheet, and the sheet model name or the sheet name defined by the user may be used.

[0080] The trained model may be recorded outside the recording device 101. For example, it may be recorded in a PC or the like connected via the input / output IF 303 and the USB port 304 of the recording device 101. The trained model is not limited to DNN, and may be a decision tree or the like. In the above embodiment, the trained model is applied to rough classification and detailed classification in the estimation of the type of the sheet S, but is not limited to this. For example, the trained model may be applied to either the rough classification or the detailed classification. Specifically, the type of the sheet S may be estimated by applying the trained model to the rough classification and deriving the index y in the detailed classification. That is, the type of the sheet S may be estimated by combining the trained model and the index y.

[0081] The present disclosure can also be realized by a process in which a program for implementing one or more functions of the above-described embodiments is supplied to a system or device via a network or a storage medium, and one or more processors in a computer of the system or device read and execute the program. It can also be realized by a circuit (e.g., ASIC) that implements one or more functions.

[0082] The disclosure of the present embodiment includes configurations typified by the following information processing device example, information processing device control method example, and program example.

[0083] <Configuration 1> An acquisition means for acquiring a characteristic value of a recording medium to be fed; a first determination means for determining the type or group of the recording medium to be fed from a recording medium group representative of a plurality of recording media and a recording medium different from the plurality of recording media, based on the characteristic value acquired by the acquisition means; a second determination means for determining a type of the recording medium to be fed from among the recording medium group when the result of the first determination means indicates that the recording medium group is the recording medium group; Equipped with 2. An information processing apparatus comprising: a recording medium supply unit that supplies a recording medium to a recording medium supplying unit that is supplied with a recording medium;

[0084] <Configuration 2> The second determination means makes the determination based on the characteristic value acquired by the acquisition means, 2. The information processing device according to configuration 1, wherein the characteristic value used for making the decision in the second decision means is the same as the characteristic value used for making the decision in the first decision means.

[0085] <Configuration 3> The information processing device according to configuration 1 or 2, characterized in that the first determination means makes the determination based on a range of values ​​indicating the characteristics of each of the recording media, derived from previously acquired characteristic values ​​of the plurality of recording media and a recording medium different from the plurality of recording media.

[0086] <Configuration 4> The information processing device according to configuration 3, wherein the second determination means makes the determination based on a range of values ​​indicating the characteristics of the plurality of recording media that is different from the range of values ​​indicating the characteristics of the plurality of recording media used by the first determination means.

[0087] <Component 5> a derivation unit that derives a value indicating a characteristic of the recording medium to be fed from the characteristic value acquired by the acquisition unit, 5. The information processing device according to configuration 4, wherein the second determination means makes the determination based on the value derived by the derivation means.

[0088] <Component 6> The information processing device described in configuration 5, characterized in that if the value derived by the derivation means is not included in any of the ranges of values ​​indicating the characteristics of the multiple recording media used by the second determination means, the second determination means determines, as the type of the recording medium to be fed, the recording medium among the multiple recording media that shows a value closest to the value derived by the derivation means among the central values ​​of the maximum and minimum values ​​indicating the characteristics of the multiple recording media.

[0089] <Component 7> The information processing device according to configuration 1 or 2, characterized in that the first decision means makes the decision using a trained model trained based on teacher data including input data corresponding to the characteristic value and first output data indicating the type of recording medium corresponding to the input data.

[0090] <Component 8> The information processing device described in configuration 7, characterized in that the second decision means makes the decision using a trained model trained based on teacher data including input data corresponding to the characteristic value and second output data different from the first output data and indicating a type of recording medium corresponding to the input data.

[0091] <Component 9> The information processing device according to any one of configurations 1 to 8, characterized in that the acquisition means acquires features relating to surface information of the recording medium being fed and features relating to cross-sectional information of the recording medium being fed, and acquires the characteristic value based on the acquired features.

[0092] <Component 10> The information processing device described in configuration 9, characterized in that the acquisition means acquires features related to the surface information from a sensor that acquires a surface image of the recording medium, and acquires features related to cross-sectional information of the recording medium from a sensor that acquires an electrical signal of ultrasonic waves that have passed through the recording medium.

[0093] <Component 11> 11. The information processing apparatus according to configuration 9 or 10, wherein the surface information includes information regarding at least one of the brightness and unevenness of the recording medium to be fed.

[0094] <Component 12> 12. The information processing apparatus according to any one of configurations 9 to 11, wherein the cross-section information includes information on at least one of a thickness and a basis weight of the recording medium to be fed.

[0095] <Component 13> 13. The information processing device according to any one of configurations 1 to 12, further comprising a display unit that displays a result determined by either the first determination unit or the second determination unit.

[0096] <Component 14> obtaining a characteristic value of a recording medium to be fed; a first determination step of determining the type or group of recording media to be fed from a predetermined group of recording media representative of a plurality of recording media and a recording medium different from the plurality of recording media, based on the acquired characteristic values; a second determination step of determining a type of the recording medium to be fed from among the recording medium group when the result of the first determination step indicates that the recording medium group is the recording medium group; A method for controlling an information processing device having 2. The information processing apparatus according to claim 1, wherein, when the type of the recording medium to be fed is determined to be one by the first determination step, the second determination step is not used to determine the type of the recording medium to be fed.

[0097] <Component 15> A program for causing a computer to execute the control method for an information processing device according to configuration 14. [Explanation of symbols]

[0098] 101 Recording device 301 CPU 305 Memory

Claims

1. An acquisition means for acquiring a characteristic value of a recording medium to be fed; a first determination means for determining the type or group of the recording medium to be fed from a recording medium group representative of a plurality of recording media and a recording medium different from the plurality of recording media, based on the characteristic value acquired by the acquisition means; a second determination means for determining a type of the recording medium to be fed from among the recording medium group when the result of the first determination means indicates that the recording medium group is the recording medium group; Equipped with 2. An information processing apparatus comprising: a recording medium supply unit that supplies a recording medium to a recording medium supplying unit that is supplied with a recording medium;

2. The second determination means makes the determination based on the characteristic value acquired by the acquisition means, 2 . The information processing apparatus according to claim 1 , wherein the characteristic value used for making the decision in the second decision means is the same as the characteristic value used for making the decision in the first decision means.

3. The information processing device according to claim 1 or 2, characterized in that the first determination means makes the determination based on a range of values ​​indicating the characteristics of each of the recording media, derived from previously acquired characteristic values ​​of the plurality of recording media and a recording medium different from the plurality of recording media.

4. 4. The information processing apparatus according to claim 3, wherein the second determination means makes the determination based on a range of values ​​indicating the characteristics of the plurality of recording media that is different from the range of values ​​indicating the characteristics of the plurality of recording media used by the first determination means.

5. a derivation unit that derives a value indicating a characteristic of the recording medium to be fed from the characteristic value acquired by the acquisition unit, The information processing apparatus according to claim 4 , wherein the second determination means makes the determination based on the value derived by the derivation means.

6. An information processing device as described in claim 5, characterized in that if the value derived by the derivation means is not included in any of the ranges of values ​​indicating the characteristics of the multiple recording media used by the second determination means, the second determination means determines, as the type of recording medium to be fed, the recording medium among the multiple recording media that shows a value closest to the value derived by the derivation means among the central values ​​of the maximum and minimum values ​​indicating the characteristics of the multiple recording media.

7. The information processing device according to claim 1 or 2, characterized in that the first determination means makes the determination using a trained model trained based on teacher data including input data corresponding to the characteristic value and first output data indicating the type of recording medium corresponding to the input data.

8. The information processing device according to claim 7, characterized in that the second decision means makes the decision using a trained model trained based on teacher data including input data corresponding to the characteristic value and second output data indicating a type of recording medium corresponding to the input data, which is different from the first output data.

9. 3. The information processing apparatus according to claim 1, wherein the acquisition means acquires a feature quantity relating to surface information of the recording medium being fed and a feature quantity relating to cross-sectional information of the recording medium being fed, and acquires the characteristic value based on the acquired feature quantities.

10. The information processing device according to claim 9, characterized in that the acquisition means acquires features related to the surface information from a sensor that acquires a surface image of the recording medium, and acquires features related to the cross-sectional information of the recording medium from a sensor that acquires an electrical signal of ultrasonic waves that have passed through the recording medium.

11. 10. The information processing apparatus according to claim 9, wherein the surface information includes information regarding at least one of the brightness and the unevenness of the recording medium fed.

12. 10. The information processing apparatus according to claim 9, wherein the cross-section information includes information on at least one of a thickness and a basis weight of the recording medium to be fed.

13. 3. The information processing apparatus according to claim 1, further comprising a display unit that displays a result determined by either the first determination unit or the second determination unit.

14. obtaining a characteristic value of a recording medium to be fed; a first determination step of determining the type or group of recording media to be fed from a predetermined group of recording media representative of a plurality of recording media and a recording medium different from the plurality of recording media, based on the acquired characteristic values; a second determination step of determining a type of the recording medium to be fed from among the recording medium group when the result of the first determination step indicates that the recording medium group is the recording medium group; A method for controlling an information processing device having 2. The information processing apparatus according to claim 1, wherein, when the type of the recording medium to be fed is determined to be one by the first determination step, the second determination step is not used to determine the type of the recording medium to be fed.

15. A program for causing a computer to execute the method for controlling an information processing device according to claim 14.

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