204 results about "Digit recognition" patented technology

The invention relates to an image recognition method, in particular to an automatic meter-reading image recognition method, and aims at solving the technical problem of low accuracy and proneness to digital recognition error. The automatic meter-reading image recognition method is simple and convenient to use, capable of accurately recognizing dial plate images and high in accuracy. The technical scheme includes the following steps: a, collecting images of a dial plate by an image collecting device; b, roughly positioning a digit indicating image; c, accurately positioning a digit indicating area image; d, accurately positioning a pointer area image; e, preprocessing the images; f, performing digit division; g, performing digit recognition; h, recognizing angle of a pointer. The automatic meter-reading image recognition method is widely used for recognition of digits of the dial plate in a remote meter reading system.

In many areas, recent developments have generated very large datasets from which it is desired to extract meaningful relationships between the dataset elements. However, to date, the finding of such relationships using prior art methods has proved extremely difficult especially in the biomedical arts. Methods for local causal learning and Markov blanket discovery are important recent developments in pattern recognition and applied statistics, primarily because they offer a principled solution to the variable / feature selection problem and give insight about local causal structure. The present invention provides a generative method for learning local causal structure around target variables of interest in the form of direct causes / effects and Markov blankets applicable to very large datasets and relatively small samples. The method is readily applicable to real-world data, and the selected feature sets can be used for causal discovery and classification. The generative method GLL-PC can be instantiated in many ways, giving rise to novel method variants. In general, the inventive method transforms a dataset with many variables into either a minimal reduced dataset where all variables are needed for optimal prediction of the response variable or a dataset where all variables are direct causes and direct effects of the response variable. The power of the invention and significant advantages over the prior art were empirically demonstrated with datasets from a diversity of application domains (biology, medicine, economics, ecology, digit recognition, text categorization, and computational biology) and data generated by Bayesian networks.

The invention provides an optical neural network method for realizing the digital recognition. The optical neural network method comprises a step of acquiring digital image features and a step of constructing an optical neural network. The optical neural network is composed of an optical interference module, an optical nonlinear module and a detector array, and the optical interference module comprises a Mach-Zehnder interferometer array and a variable optical attenuator, and can realize any matrix multiplication. The optical non-linear module is composed of a saturable absorber and other devices with the non-linear effects, and can realize the function of an activation function in an artificial neural network. According to the method, the calculation time is shortened through optical calculation, and the calculation energy consumption is reduced.

The invention provides a recognition method for English words and digits in a natural scene image. According to the method, recognition of the English words and the digits in a natural scene is divided into a feature extraction step, a feature focusing step and a feature recognition step, wherein a convolutional neural network is utilized to perform feature extraction on an input image, an attention mechanism is utilized to focus on useful information in a feature sequence, a long and short-time memory network is utilized to recognize feature vectors, therefore, a deep neural network and the attention mechanisms are combined, and a final recognition result can be obtained directly when the image is input into the deep neural network. Through the method, it is not needed to perform winding sliding operation on the input image or recognize characters in a window; and meanwhile, an output string is the final recognition result, so that it is not needed to integrate recognized strings through a merging algorithm.

The invention provides a digit recognition method for identification cards. The digit recognition method comprises the following steps: acquiring a classifier training file; transforming a color image to be identified into a gray level image; accurately positioning digit element positions of each identification card; accurately segmenting digit characters of each identification card; verifying whether a candidate region is a real digit region of each identification card; carrying out digit recognition on the basis of a nearest neighbor algorithm; judging whether the current digit element is the last group of digit element. According to the digit recognition method provided by the invention, positioning, segmentation and identification of digits on the identification cards are carried out by adopting an image processing technology; the recognition precision is relatively high, no special equipment is needed and the cost is low; the digit recognition method has strong applicability to various environments and can be used for reading information of multiple identification cards simultaneously.

The invention relates to amethod and device for identifying the digital image displayed in the mechanical instrument window, suitale for the identification of figure for various mechanical instrument window. The method, based on the working principle of absolute encoder in the field of industrial sensor, applys the mechanical location identifying method to the field of identifying image, carry out the identification for the position of image to reach the identification for the content of image, and especially is suitale for the application in which the one-chip microcomputer is as a processor, with high efficiency, accurate identification and good versatilityin. The device adopts the one-chip microcomputer as processor and is an integer of collecting, processing, identifying and transmitting image, and characterized in that the device comprises an image collection module, one-chip microcomputer processing and identifying module, a communication interface, a power module and transparent sealing structure. The device, having low cost, high efficiency and little volume, can be installed above the various mechanical instrument window with other structure, accordingly the identification of figure displayed in the instrument window can be carried out, as well as the date and the image are transmitted at the same time.

The present invention provides a handwritten digital recognition method and system based on depth learning. A convolutional neural network is trained with a sample set which is formed by constructing a handwritten digital picture with a label, the trained convolutional neural network is saved, and the image to be identified is taken as the input, and according to the output vector, the recognition result is obtained. The handwritten digital image is identified through the convolution neural network, so that the displayed feature extraction is avoided, the picture is directly taken as the network input, and the recognition accuracy is high. The network can be repeatedly used after being trained, the processing efficiency is high and the training time is short.

The invention relates to a floating number plates discriminating method, which is based on the three-road parallel manmade nerve-net. The identify steps are that adopting the video burst mode to catch the video automatically for the collection of video pictures of the moving vehicle, character cutting the part of pictures of the number plates and then being the input signal of the nerve-net; said nerve-net adopts three standard self-adapt vibrate net each of which has its own work, the three nets are the Chinese characters identify net, the English characters identify net and the data identify net, all of which identify the inputted vector signals at one time, and outputs the most similar sort unity respectively, through controlling the property of the Territory value and the identify accurately according to the number plates and filter, at last add a color property of number plates as the output of the discriminating results. Adopting the method of the invention can identify the number plates of vehicle fast and accurately under the largest driving speed of the vehicles.

The invention discloses a method for inputting graphic random passwords, which relates to a password input method for a keyboard-less large-screen touch screen mobile device. The method is characterized in that a digital recognition unit for transmission is replaced by a graphic recognition unit which has no mutual logical relation with the digital recognition unit; and when a user enters into a password input unit, a view dialog box which is formed by n*m matrixes is popped up by a login program, the view dialog box is used for inputting passwords, a specific value defined by a graph is generated in a system memory after the graph is clicked by the user, the passwords can be formed after the graph is clicked by the user for a couple of times, the selection function can be stopped by a system if one click during a clicking process is long press, a dialog box can be hidden, the passwords can be transmitted to a background verification unit, the system can be logged in if the passwords are right, and the user can be noticed to input the passwords again if the passwords are wrong. When the user enters into a password selection dialog box for the next time, the matrixes in the password dialog box are rearranged, the positions clicked by the user when the user logs in the system in each time are random, graphs without mutual logical relations are additionally used for distinguishing, thus the passwords input in each time seem different by others, and the safety of the passwords is increased.

A method of identifying or authenticating a product by providing an analog identification indicium including a randomized pattern of identification features on a first part of the product wherein one or more attributes of the randomized pattern of identification features correspond to an item identifier. A digital identification record is provided on a second part of the product including an encoded digital version of the item identifier, the first and second parts being separable when the product is used. The randomized pattern of identification features of the analog identification indicium is read and decoded to generate an item identifier and the digital identification record is read and decoded to generate an item identifier. If a special light source is necessary to read the randomized pattern of identification feature, a flag is present in the analog identification indicium or the digital identification record that indicates one or more types of light sources that can be used to read the identification feature. The appropriate light source is activated to read the identification feature and if the appropriate light source is not available the user is alerted to this fact. The product is deemed authentic if the item identifier from the analog identification indicium substantially matches the item identifier from the digital identification record.

The invention discloses a digit recognition method based on the lip texture structure. By making use of the strong feature extraction ability of a convolution neural network and the temporal information processing ability of a long short-term memory network in deep learning, a digit is recognized based on lip movement of an object in a video through the convolution neural network and the long short-term memory network. The method is of strong robustness to the within-class difference of lip images, the head posture change, and the illumination change under a non-control environment. The problem that the lip language recognition technology is of low recognition accuracy in interactive in-vivo detection is solved. The method can be widely used in a scene equipped with a high-resolution camera, such as interactive in-vivo detection of the China's financial system.

A system for augmenting the transmission of audio and data information from a console (45) to a network (48). The system utilizes a data packet format that permits the detection and capture of relatively more detailed information from raw signal data that may otherwise not be available during the course of standard local and remote radio control operations. The system includes the ability to automatically detect Automatic Numerical Identification (ANI) information from the raw signal for recording or display purposes. The ANI information is stored on and received from a central server (44), thereby eliminating the need for each console (45) to store and access an individual ANI / user cross reference table. The system automatically analyzes the data contained in each packet to differentiate between identification and audio information, thereby reducing the total amount of data processed by eliminating the introduction of audio processing or buffering steps into the nonaudio control and identification data. Bypassing of the network jitter buffer (61) permits multicasting of only a brief packet of control data to ensure reception by all devices on the network (48). Actual audio information present in the raw signal data is detected and used to mute receive path audio packets arriving at the console (45), thereby eliminating the need for a dedicated echo canceller while a console operator is transmitting.

The invention relates o an identification method for handwritten numbers. The method comprises the following steps of preprocessing number images, wherein the preprocessing step comprises the substepsof graying the number images, carrying out binaryzation on the number images, carrying out image denoising, segmenting character strings, carrying out number normalization, and carrying out number refinement; and setting up a deep convolutional neural network model, configuring neural network parameters, generating training set samples and test set samples, adjusting the parameters, training thenetwork model, and identifying the numbers through the trained network mode. According to the method, through preprocessing of the image, influences of noises on the image can be prominently reduced,and the images with different sizes can be normalized into the images with the same size; through utilization of the deep convolutional neural network, the training set samples and the test set samples are generated, the parameters are adjusted, the network is trained, and the numbers are identified through utilization of the trained network model. The handwritten number identification accuracy and rate can be improved under the condition that the complexity is similar, and the method can be widely applied to the fields such as post office letter sorting and bank check inputting.

The present invention discloses a multi-feature fusion and deep learning network extraction-based handwritten digit recognition method. The method includes the following steps that: the image data of handwritten digits are read, vectorization pre-processing is performed on the data; multi-feature fusion is performed on the processed data by using the principal component analysis (PCA) technique and the histogram of oriented gradients (HOG) technique, so that shallow compound features can be constructed; secondary feature extraction is performed on the data which have been subjected to multi-feature fusion through using a shallow stack auto-encoder (SAE) model, so that a deep learning network can be constructed to perform high-level and deep learning and processing on the shallow compound features; and the Softmax classifier is used to test classification effects. According to the multi-feature fusion and deep learning network extraction-based handwritten digit recognition method of the invention, the multi-feature fusion method is adopted, the PCA technique and the HOG technique are integrated, so that the shallow compound features can be constructed; the SAE model is adopted to perform secondary feature extraction, so that the deep learning network can be constructed, and simpler and more efficient feature samples can be obtained; and the Softmax classifier is used to test the classification effects; and therefore, the recognition accuracy rate of handwritten digits can be increased to 99.2%.

The invention discloses a SqueezeNet based handwritten digital recognition method. The SqueezeNet based handwritten digital recognition method specifically comprises: (1) pre-processing recognized handwritten digital samples; (2) constructing a SqueezeNet convolutional neural network model; (3) training the SqueezeNet network model, and testing the handwritten digital samples; and (4) obtaining arecognition result. Compared with the traditional AlexNet, the SqueezeNet network model in the invention effectively reduces the total number of network parameters, and improves the accuracy of handwritten digital recognition, has the basis of model compression, and can be better applied to mobile devices, distributed training and embedded hardware.

A digital method and system allowing crystal identification in radiation detector machines is described. The crystal identification is based on recognition of radiation detector signal shape through discrimination of detector signal's dynamic characteristics. The digital method is based on recursive and non-recursive algorithms, such as adaptive filtering combined or not with quantization methods. These digital algorithms, commonly used in other engineering applications, were modified and tailored for radiation detection. Although the method was specially designed for crystal identification measurement, which is exemplified here, it can effectively recognize the detector signal shape in any radiation detection context.

A graphical dynamic password inputting and verifying method used for a touch screen mobile device without a keyboard is characterized in that a graphical recognition unit is used for replacing traditional digital recognition so that a user can remember conveniently, and each graph group has one code used for identifying the graph group. A password is generated according to the steps that options in each group are arranged in a user registration process; a system generates a corresponding character for each option; after a user finishes setting, a character array is formed; the system marks each character of the character array with one integer to form a key-value pair group which is used as an original password and transmitted to a server database to be saved. When the user logs in next time, a mobile terminal generates a random array randomly, and then reminds the user of inputting the password according to the graph code mapped by the random array. A server judges whether the password is input correctly according to the mapping relation between the original password matched with the random array and a sub password. By means of the graphical dynamic password inputting and verifying method, the passwords input by the user every time are basically different, and therefore the safety of the passwords is enhanced.

An acoustic modeling method of Chinese continuous digit identification includes setting up individual model for different initial consonant and simple or compound vowel of Chinese continuous digit, defining context relativity and its acoustic model to accurately describe voice of Chinese continuous digit, applying hidden Markov model as basic model presentation and utilizing cluster algorithm to carry out train of model parameter for obtaining acoustic model of continuous digit. The method and system of search-decode as well as acoustic modeling system are also disclosed.

Embodiments of methods of identifying structural traps are disclosed herein. Embodiments of the disclosed method use an alternative approach to boundary value problem solvers, based on simple mathematical geometry adapted to the nature of the acquired data. Embodiments of the method rely on digital elevation model type of data (i.e. any given subsurface horizon has only single elevation data values). The disclosed methods allow for a significant algorithm speed-up in identifying structural traps especially when handling large and high density datasets which was heretofore not possible with existing methods.

A device and method of verifying protective case usage, is described. The device includes one or more processors to drive an electromechanical transducer with an input signal. The electromechanical transducer generates an input force based on the input signal, and a sensor of the device generates a test output signal in response to the input force. The one or more processors determine, based on the test output signal, a level of verification that a protective case is mounted on the device. The determination can include determining whether the test output signal matches a predetermined impulse response signal indicative of an unprotected device or a protected device. A digital identification tag of the protective case can be read by a radio-frequency transceiver of the device to provide an additional level of verification that the protective case is mounted on the device. Other aspects are described and claimed.

An embodiment of the invention discloses a handwritten digit recognition method and system. During dimensionality reduction of handwritten digits, all image data are represented linearly through K neighbors, weighting coefficients of all image data when the image data are represented linearly through the K neighbors are obtained through an orthogonal matching algorithm, besides, weighting coefficient matrixes are established to perform dimensionality reduction on training image data, and dimensionality reduction is performed on images to be recognized through weighting coefficient vectors and vector data of the K neighbors of the images after dimensionality reduction. According to experiments, by the aid of the handwritten digit recognition method, the recognition rate of handwritten digit recognition is improved.

The invention discloses a handwritten numeral recognition method based on point density weighting online FCM clustering. The method is used for processing the large-scale offline handwritten numeral recognition problem. The method includes the steps that (1), all handwritten numeral image sets are preprocessed; (2), clustering centers are initialized, and data points are made to sequentially enter processing procedures; (3), the membership degree of the current data point and all the clustering centers is calculated; (4), if the membership degree reaches a threshold value, the position of the nearest clustering center is updated; (5), if the membership degree does not reach the threshold value, the current data point is not processed and is temporarily placed in a to-be-processed region; (6), when the to-be-processed region reaches certain standards, data in the to-be-processed region are clustered through a point density weighting FCM algorithm, and the clustering centers are updated; (7), circulation continues until all the data points are processed; (8), the membership degrees of all the data points are calculated through acquired clustering center blocks, the data points are divided into different classes, and data classification is finished through scanning at a time. According to the method, the space complexity and the time complexity can be lowered from the aspect of processing the large-scale handwritten numeral recognition problem.

A brain-computer interface (BCI) designed with a hybrid electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) for letter and number recognition system for people who cannot speak. By this system, the words and numbers a subject thinks of are reflected on a display screen. A speech tool has been designed for these individuals allowing them to express themselves.

The invention discloses a multifunctional digital meter automatic reading method based on a target detection algorithm, and the method comprises the following steps: S1, carrying out the lightweight improvement of a YOLOv4 network model, and obtaining a YOLOv4 lightweight network model; S2, collecting the multifunctional digital table picture, and performing position calibration and category calibration on numbers on the multifunctional digital table picture; and S3, training the YOLOv4 lightweight network model by using the multifunctional digital table picture calibrated in the step S2 and the calibration information of the multifunctional digital table picture to obtain a YOLOv4 lightweight digital detection model. The multifunctional digital meter automatic reading method based on thetarget detection algorithm is high in recognition speed, high in recognition accuracy and good in stability, and the problems that an existing digital meter automatic reading method is prone to beingaffected by external environment changes and image imaging quality and lacks stability and accuracy of digital recognition are solved.

The invention discloses a handwritten digit recognition method and device on the basis of image covariance characteristics. The method includes: extracting covariance matrixes of N handwritten digit training images, and using the covariance matrixes as training samples, wherein N is a natural number; utilizing a support vector machine to train the training samples to obtain classifier model coefficients, wherein the kernel function adopted by the support vector machine is a matrix kernel function for calculating similarity among the covariance matrixes; utilizing the classifier model coefficients, the matrix kernel function and the training samples to build a classifier model; utilizing the classifier model to recognize handwritten digit images which need to be recognized. The support vector machine adopting the matrix kernel function can be used for processing the image covariance characteristics, therefore the generated classifier model coefficients are more accurate, and recognition rate of handwritten digits is improved further.

The invention provides an accurate positioning and recognition method for a train number on the basis of deep learning. The accurate positioning and recognition method comprises the steps of: collecting a train panoramic image, and carrying out size adjustment on the train panoramic image; constructing a train number positioning network, and taking an adjusted panoramic image as a training set totrain the train number positioning network; training a train number identification network according to the train number area output by the train number positioning network; performing size adjustmenton a to-be-identified train panoramic image, and inputting an adjusted train panoramic image into a trained train number positioning network so as to obtain an accurately-positioned train number area; and inputting the train number area into a trained train number identification network for identification so as to obtain a train number digital identification result. With the accurate positioningand recognition method provided by the invention, the train number sequence with any length can be processed; the defects of low positioning accuracy and difficulty in distinguishing small-size vehicle numbers for manual features in a complex scene and a conventional deep learning method are overcome; meanwhile, character segmentation is not involved; and overall recognition is achieved.

The invention relates to the technical field of image recognition, in particular to a handwritten numeral recognition method based on feature dimension reduction, which comprises the following steps:obtaining a handwritten numeral image for recognition, and obtaining a preprocessed handwritten numeral image after image preprocessing; extracting handwritten numeral features in the preprocessed handwritten numeral image through feature extraction processing; through feature dimension reduction processing, reducing the feature dimension of the handwritten numeral features, and obtaining low-dimension handwritten numeral features; and performing feature recognition processing on the low-dimensional handwritten numeral features to obtain a handwritten numeral recognition result. According to the handwritten numeral recognition method based on feature dimension reduction, feature dimension reduction processing can be carried out on handwritten numeral features so as to reduce the difficultyand complexity of feature recognition, and therefore the recognition effect and efficiency of handwritten numerals can be improved.

The invention discloses a depth learning based smart instrument detection method. The method includes taking a picture of an instrument through a camera, performing preprocessing on the image throughan IPC and reading instrument numerical value through image number recognition technology; performing analysis on the read instrument numerical value data and making result prediction and judgment; sending an instrument change alarm if instrument damage is judged through analysis and taking corresponding halt treatment; sending an alarm and stopping equipment operation immediately if the instrument works normally but the numerical value surpasses a safety range of the instrument; performing smart learning on history data and previous judgment and returning to the system, improving data analysis and system pre-judgment continuously, improving the pre-judgment accuracy through uninterrupted machine self learning. The method provided by the invention has advantages of high smart level and good pre-judgment capability. Besides, the pre-judgment method is improved continuously through history data and history judgment and the smart pre-judgment capability is improved.

The present invention provides a method for IC identification. It can be used to identify the origin of the IC design, wherein said IC comprises at least a testing circuit for testing the functional correctness of said IC, and said testing circuit is activated by a testing activation signal. The testing circuit, after receiving a testing signal, will generate a testing result. The identification method comprises the steps of (1). providing an original identification data representing the origin of the IC; (2). transforming the original identification data into a digital identification data; (3). providing an identification circuit for generating the digital identification data, wherein the identification circuit is activated by the testing activation signal, and generates the digital identification data; (4). integrating the identification circuit and the testing circuit into a composite circuit, wherein the composite circuit will be activated by the testing activation signal, receive the testing signal, process the digital identification data from identification circuit and the testing result from the testing circuit, and generate an output signal; (5). inputting the testing activation signal and the testing signal to the composite circuit, and waiting for the output signal; (6). receiving the output signal and processing the output signal to obtain digital identification data; and (7). interpreting the digital identification data to obtain the original identification data.