A method and system for correcting errors in paper equipment operation record recognition results

Abstract

The present invention discloses a method and system for correcting errors in the recognition results of paper-based equipment operation records. The method first scans the paper-based equipment operation record, performs character recognition on the scanned record using optical character recognition (OCR) technology, and assesses the confidence level. The method then performs layered error detection on the recognition results: identifying characters with low confidence levels, characters that do not conform to the column pattern, and using an outlier detection method to identify and mark abnormal characters in all columns. Finally, the character that is most similar to the erroneous character and conforms to the column pattern is selected from a candidate character set for correction, thereby generating an accurate equipment operation record table. The method of the present invention, targeting the digitization of paper-based equipment operation record documents, combines the content and organization of the equipment operation record data to locate and correct errors in the recognition results, thereby reducing the workload of proofreading during the digitization of the equipment operation record table.

Description

Technical Field

[0001] The present invention relates to the technical field of image recognition, and in particular to a method and system for correcting errors in recognition results of paper-based equipment operation records. Background Art

[0002] Paper documents, especially equipment operation records, play a crucial role in business operations. These records detail key operating parameters such as pressure, temperature, and speed, which are crucial for equipment maintenance, troubleshooting, and safety precautions. With technological advancements, optical character recognition (OCR) and form recognition technologies have become effective means of digitizing these paper documents. The application of deep learning, in particular, has significantly improved recognition accuracy, laying the foundation for the digital transformation of paper documents.

[0003] Existing OCR technologies use deep learning models to recognize and convert text within documents. Using predefined character sets and patterns, paper documents are scanned and converted to digital format using OCR technology for further analysis and processing. These models are capable of processing a wide range of character types, including Chinese characters, numbers, uppercase and lowercase English letters, and special symbols. However, these technologies still face challenges when processing handwritten text and text with specialized formats.

[0004] Although existing OCR technology has achieved significant progress in recognition accuracy, it still faces some problems and limitations in practical applications. First, the fixed recognition range of the OCR model limits its flexibility in specific application scenarios, which may lead to the misrecognition of unexpected characters. Second, the OCR model lacks accuracy in recognizing handwritten text. Differences in writing habits between individuals and inconsistent document quality, such as unclear scans, all increase the difficulty of recognition. These issues not only limit the overall effectiveness of the OCR system, but also increase the workload of subsequent proofreading work, affecting the efficiency of the digitization process. Summary of the Invention

[0005] The present invention proposes a method and system for correcting the recognition results of paper-based equipment operation records, which solves the problems of existing optical character recognition methods that are insufficiently accurate in recognizing handwritten text and lack specificity in equipment recording scenarios.

[0006] To solve the above technical problems, the present invention provides a method for correcting errors in recognition results of paper-based equipment operation records, comprising the following steps:

[0007] Step S1: Scan and identify the paper equipment operation record to obtain an equipment operation record table and the confidence level corresponding to each character in the equipment operation record table;

[0008] Step S2: Perform hierarchical error detection and marking on the recognized device operation record form:

[0009] Mark the characters with a confidence level less than the set confidence threshold in the device operation record form as error characters;

[0010] Mark the characters that do not conform to the preset column pattern in each column of the device operation record form as error characters;

[0011] Take each column of the device operation record form as a time series, calculate the characteristics of each time series, regard the time series with characteristics greater than the set characteristic threshold as an abnormal series, calculate the relative distance between characters in the abnormal series, and mark the characters with a relative distance greater than the set distance threshold as error characters;

[0012] Step S3: Select the character with the highest similarity to the error character and conforming to the column pattern of the column where the error character is located from the preset candidate character set as the correction character, and obtain the corrected device operation record form.

[0013] Preferably, in step S2, the calculation of the characteristics of each time series and regarding the time series with characteristics greater than the set characteristic threshold as an abnormal series includes the following steps:

[0014] Step S201: Convert a column of characters with length n into a time series:

[0015] X(t) = (x(t1), x(t2), x(t3),..., x(t[[ID=2)]] n ));

[0016] In the formula, is the character at time t i moment;

[0017] Step S202: Use a sliding window with length w and w < n to perform equal-length segmentation on the time series, with a sliding step of r, and continuously slide (n - w) / r times to obtain (n - w) / r + 1 sub-time series with length w;

[0018] Step S203: Calculate the confidence interval radius of the slope of each sub-time series as the characteristic of the sub-time series, and regard the sub-time series with a confidence interval radius greater than the set confidence interval threshold as an abnormal series. x(t i )

[0019] Preferably, in step S203, the calculation of the confidence interval radius of the slope of each sub-time series includes the following steps:

[0020] Step S2031: Calculate the slope of each character in the sub-time series to obtain w - 1 slope values;

[0021] Step S2032: Calculate the mean and mean square error of the slope of each sub-time series;

[0022] Step S2033: Calculate the confidence interval radius of the slope of each sub-time series. The expression for calculating the confidence interval radius is:

[0023]

[0024] Where, d j is the radius of the confidence interval; θ j↑ is the upper confidence limit; θ j↓ is the lower confidence limit; s j is the mean of the slope; δ j is the mean square error of the slope; Z is a random variable that satisfies the normal distribution; α is the confidence level.

[0025] Preferably, in step S2, the distance between each character in the abnormal sequence and other characters is calculated, and characters with a distance greater than a set distance threshold are regarded as erroneous characters, including the following steps:

[0026] Step S211: calculating the reachable distance of each character in the abnormal sequence, and calculating the local density of each character according to the reachable distance;

[0027] Step S212: calculating the local outlier factor of each character according to the local density;

[0028] Step S213: taking 1 / 2 of the maximum value of the local outlier factor as a distance threshold, and taking characters whose local outlier factor is greater than the distance threshold as erroneous characters.

[0029] Preferably, the expression for calculating the reachable distance in step S211 is:

[0030] RD k (p,q)=max{k-distance(q),dist(p,q)};

[0031]

[0032] Where, RD k (p,q) is the reachable distance between character p and character q; k-distance(q) is the distance between character q and the kth character closest to character q; dist(p,q) is the Euclidean distance between character p and character q; N is the dimension of the character.

[0033] Preferably, the expression for calculating the local density in step S212 is:

[0034]

[0035] Where, LRD k (p) is the local density of character p; N k-distance (p) is the set whose distance to character p is less than k-distance(q).

[0036] Preferably, the expression for calculating the local outlier factor in step S213 is:

[0037]

[0038] Where LOF(p) is the local outlier factor of character p.

[0039] Preferably, calculating the similarity between the erroneous character and all characters in the candidate character set of the column to which the erroneous character belongs in step S3 includes the following steps: converting the erroneous character and all characters in the candidate character set into binary images, calculating the intersection-and-union ratio between the binary image of the erroneous character and the binary images of all characters in the candidate character set, and using the intersection-and-union ratio as the similarity between the erroneous character and the characters in the candidate character set.

[0040] Preferably, in step S2, a corresponding regular expression is designed for each column in the equipment operation record table according to the pattern of each column character, as the column pattern of each column.

[0041] A system for correcting errors in recognition results of paper equipment operation records, applicable to the above-mentioned method for correcting errors in recognition results of paper equipment operation records, comprising a character recognition module, an error location module, and a character correction module;

[0042] The character recognition module converts the text on the paper equipment operation record into characters using OCR technology, and outputs the equipment operation record and the confidence level corresponding to each character in the equipment operation record;

[0043] The error location module includes a confidence error location module, a column pattern error location module and a time series outlier location module;

[0044] The confidence error positioning module is configured to mark characters in the equipment operation record table whose confidence is lower than a set confidence threshold as error characters;

[0045] The column pattern error locating module checks whether the recognition result of each column character of the equipment operation record table conforms to a preset column pattern, and marks characters that do not conform to the column pattern of the corresponding column as error characters;

[0046] The time series outlier location module treats each column of characters in the equipment operation record table as a time series, and uses an outlier detection method to identify and mark all abnormal data in the time series as erroneous characters;

[0047] The character correction module calculates the similarity between the erroneous character and the characters in the candidate character set, selects the character with the highest similarity as the correction candidate, ensures that the selected character conforms to the column mode of the column to which the erroneous character belongs, and outputs a corrected equipment operation record table.

[0048] The benefits of the present invention include at least:

[0049] 1. Through the preset column mode, it can more accurately recognize text with specific formats or patterns, such as date, time, temperature, etc. in equipment operation records, thus solving the problem that existing OCR technology is not targeted enough when processing text with specific formats;

[0050] 2. Combining confidence assessment and time series analysis can more effectively identify and correct errors in handwritten text. This allows the method to maintain a high recognition accuracy even when processing lower-quality scanned documents, reducing the reliance on high-resolution scanned documents and improving the recognition accuracy of handwritten text.

[0051] 3. Through the multi-level error detection and correction process, the error detection and correction process is made more systematic and automated, which significantly improves the practicality of the OCR system, reduces the proofreading workload caused by recognition errors, and thus improves overall work efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 Schematic diagram of a method flow in an embodiment of the present invention. DETAILED DESCRIPTION

[0053] The following is a clear and complete description of the technical solutions in the embodiments of the present invention, in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts are within the scope of protection of the present invention.

[0054] Paper-based equipment operation records are often organized in tables. A single document page consists of one or more tables, each of which records different equipment operation data, or each table records the operation data of different components of the same equipment. Tables consist of a header and table contents. The header is usually printed text, describing the contents of each column; the table contents are handwritten numbers, English letters, or special characters.

[0055] Because everyone has different writing habits, the way they write the same character or number can vary greatly, making it difficult to scan and recognize paper equipment operation records using optical character recognition (OCR). Furthermore, the low quality of paper documents and unclear scans further increase the difficulty of recognition.

[0056] Without solving the above problems, the embodiments of the present invention provide Figure 1 A method for correcting errors in paper equipment operation record recognition results is shown, comprising the following steps:

[0057] Step S1: Scan and identify the paper equipment operation record to obtain a digital equipment operation record sheet.

[0058] Specifically, after scanning the paper equipment operation record, OCR recognition and table structure recognition technology are used to save the paper equipment operation record table in Excel file format.

[0059] Step S2: performing layered error detection and marking on the identified equipment operation record table.

[0060] Specifically, the manifestations of OCR recognition errors are mainly in two categories: column pattern mismatch and column logic non-compliance. Column pattern refers to the same pattern that the data in the same column of the equipment operation record table conforms to. Column pattern mismatch refers to the situation where the recognition result does not meet the semantic specification due to the recognition error of the OCR algorithm. For example, if the column pattern to which a column of data belongs is equipment temperature, and the OCR algorithm recognizes numbers as English characters and decimal points as English letters, etc., the recognition result does not match the column pattern of the equipment temperature column. Column logic non-compliance means that the OCR recognition result meets the pattern of the column to which it belongs, but due to the OCR recognition error, the decimal point is lost in the recognition result, resulting in the recognition result being too large, one more number is recognized, resulting in the recognition result being too large, one less number is recognized, resulting in the recognition result being too small, etc., resulting in a situation that is obviously inconsistent with the business logic of the column.

[0061] Aiming at the manifestation of recognition result errors, a hierarchical error detection and location scheme is proposed:

[0062] First layer: Error detection and location for column pattern mismatch.

[0063] Equipment operation logs record the date, time, and value of equipment operating parameters. These parameters can be physical parameters such as the temperature and pressure of equipment components, and these parameters exhibit distinct patterns. To address this, corresponding patterns can be designed for each type of equipment operating parameter, including date patterns, time patterns, pressure patterns, temperature patterns, and numeric patterns. The set of column patterns can also be expanded based on actual application scenarios. A corresponding regular expression is defined for each column of data to form a column pattern.

[0064] When performing error detection and location, we first obtain a column of characters in the equipment operation record table, determine the mode of each cell in this column of characters, use a voting algorithm to obtain the column mode of the column, mark the characters that do not match the column mode as error characters, and complete the error location.

[0065] The second layer: confidence-based error detection and localization.

[0066] The final layer of the OCR recognition network performs an N-classification task, where N is the number of characters to be recognized. From the perspective of the classification network's loss function, the more accurately a character is predicted, the higher the probability P of that character being output in the softmax function of the classification task. The OCR recognition result returns a probability P, known as the confidence level. A higher confidence level indicates a higher accuracy rate for character recognition. Based on this principle, confidence levels are used as prior knowledge to detect and locate recognition errors. An appropriate confidence threshold is set, and characters with a confidence level below the threshold are marked as incorrect, drawing the attention of proofreaders.

[0067] The third layer: outlier error localization based on sliding window.

[0068] Even if the confidence level of a character recognition result exceeds the set threshold and matches the column pattern, it's still possible that the character was misidentified. For example, the temperature data "32.5" might be mistakenly recognized as "325." In equipment operation logs, each column of operating parameters is typically recorded in chronological order, so a column of data can be considered a time series. These time series typically follow specific patterns of change and exhibit consistent characteristics.

[0069] Therefore, outlier detection methods in time series can be used to detect and locate errors. This paper proposes a time series outlier detection algorithm based on a sliding window for detecting and locating erroneous data.

[0070] The algorithm is divided into two stages:

[0071] The first stage: Detection of abnormal sub-time series. In this stage, the entire time series is segmented into multiple shorter sub-time series by using the sliding window technique. Then, features are extracted from each sub-time series, and it is detected whether these features exceed the set feature threshold. If the features of a certain sub-time series exceed the threshold, then it will be marked as an abnormal sub-time series. This stage specifically includes the following steps:

[0072] S1: Convert a column of characters with length n into a time series:

[0073] X(t) = (x(t1), x(t2), x(t3),..., x(t n ));

[0074] In the formula, x(t i ) is the character at time t i , 1 ≤ i ≤ n, and the acquisition times are strictly increasing.

[0075] S2: Use a sliding window with length w, where w < n, to equally segment the time series with a sliding step of r, and slide continuously (n - w) / r times to obtain (n - w) / r + 1 sub-time series with length w.

[0076] S3: Calculate the confidence interval radius of the slope of each sub-time series as the feature of the sub-time series, including the following steps:

[0077] S31: Let a sub-time series of the time series X(t) be represented as X j (t), and calculate the slope between any two adjacent data points in this sub-time series according to the following formula:

[0078]

[0079] In the embodiment of the present invention, the sliding step is set to 1, that is, t i -t i-1 = 1.

[0080] Calculate the slope of each character in the sub-time series X j (t) to obtain w - 1 slope values;

[0081] S32: Calculate the mean s j and the mean square deviation δ j of the slope of each sub-time series:

[0082]

[0083] S33: Calculate the confidence interval radius of the slope of each sub-time series. The expression for calculating the confidence interval radius is:

[0084]

[0085]

[0086] Where, d j is the radius of the confidence interval; θ j↑ is the upper confidence limit; θ j↓ is the lower confidence limit; s j is the mean of the slope; δ j is the mean square error of the slope; Z is a random variable that satisfies the normal distribution; α is the confidence level.

[0087] S4: The sub-time series whose confidence interval radius is larger than the set confidence interval threshold is regarded as an abnormal series. The confidence interval threshold is generally set based on empirical values ​​or after statistics of normal data.

[0088] Phase 2: Outlier Detection. After detecting anomalous sub-time series, the Local Outlier Factor (LOF) algorithm is used to identify outliers within these sub-time series. The LOF algorithm identifies outliers by evaluating the local density deviation of data points, thereby accurately locating data points that do not conform to the expected pattern. This phase specifically includes the following steps:

[0089] S1: Calculate the reachable distance of each data point in the anomaly sequence:

[0090] RD k (p,q)=max{k-distance(q),dist(p,q)};

[0091]

[0092] Where, RD k (p,q) is the reachable distance between point p and point q; k-distance(q) is the distance between the kth data point closest to the test data point q and the test data point q; dist(p,q) is the Euclidean distance between point p and point q; N is the dimension of the data point.

[0093] S2: Calculate the local density of each data point based on the reachable distance:

[0094]

[0095] Where, LRD k (p) is the local density at point p; N k-distance (p) is the set of data points whose distance to point p is less than k-distance(q).

[0096] S3: Calculate the local outlier factor of each data point based on the local density. If the local outlier factor of a data point is greater than 1, it means that the density of the data point is smaller than the average density of the data points in its neighborhood, and the data point is more likely to be an outlier point. Otherwise, it means that the data point is a normal point. The expression for calculating the local outlier factor is:

[0097]

[0098] S4: Use 1 / 2 of the maximum value of the local outlier factor as the distance threshold, and regard characters whose local outlier factor is greater than the distance threshold as error characters.

[0099] Step S3: For each erroneous character: select a character from a preset candidate character set that has the highest similarity to the erroneous character and conforms to the column pattern of the column to which it belongs as a correction character, and obtain a corrected equipment operation record table.

[0100] Specifically, the error types of the recognition results include column pattern mismatch, low confidence, and column logic failure. Low confidence is likely to occur in column pattern mismatch or logic failure. This embodiment of the present invention uses the column pattern mismatch error type to illustrate the error correction process.

[0101] Column pattern mismatches often occur when numbers are recognized as characters, characters are recognized as numbers, punctuation marks are recognized as letters, or letters and numbers are recognized as punctuation marks. After locating the incorrect characters based on the column pattern, the first step is to use the text dot matrix similarity to determine the corrected character candidates. Then, the candidates that do not match the column pattern are filtered out. Finally, the corrected character candidate with the greatest similarity and that matches the column pattern is selected as the corrected character. The following steps are included:

[0102] S1: Design a candidate character set based on the device operation record scenario.

[0103] First, a candidate character set is designed based on the actual scenario. This candidate character set is defined as characters that may appear in the OCR recognition results that require error correction. This invention targets device operation record data, so the candidate character set includes numbers, English characters, punctuation marks, and symbols for physical parameters such as temperature and pressure. For example, if the scenario involves recognizing device operating parameters such as pressure and temperature, the candidate character set would include numbers, pressure symbols, temperature symbols, etc.

[0104] S2: Calculate the text dot matrix similarity between the wrong character and the candidate character set.

[0105] Each character in the candidate character set is represented by a dot matrix of a fixed size, and the dot matrix of each character is a binary image. The foreground of the binary image is black, and the background is white. The values ​​of the pixels corresponding to the foreground of the binary image are set to 1 or 255, and the values ​​of the pixels corresponding to the background are set to 0, and the segmentation result of an image is obtained. The similarity measurement of two characters is to use the dot matrix image of one character as the "gold standard" of image segmentation, and the dot matrix image of the other character as the segmentation result. The similarity of the two characters is measured by calculating the intersection-over-union ratio between the segmentation result and the "gold standard", that is, A similarity threshold is set, and characters with similarity greater than the set similarity threshold are selected as the correction character candidate set.

[0106] S3: Filter the corrected character candidate set by column pattern: Delete the characters in the corrected character candidate set that do not conform to the column pattern to which the incorrect character belongs.

[0107] S4: Sort the characters in the filtered correction character candidate set according to their similarity to the wrong character, and use the character with the highest similarity as the correction character.

[0108] Example 1

[0109] The method of the present invention is used to identify and correct time series, including the following steps:

[0110] S1: Set a candidate character set according to the time recognition scenario. The candidate character set includes numbers 0-9 and the ":" symbol, and generate a dot matrix image of these 11 characters at the same time.

[0111] S2: Design a column pattern for the time column. The regular expression for the column pattern of the time column is: ^[0-2][0-9]:[0-5][0-9]:[0-5][0-9]$.

[0112] S3: Layered error detection and localization:

[0113] First layer: Set the text recognition confidence threshold to 0.90. The confidence returned by a cell in the time column, "12:75:49," is 0.95, which meets the confidence requirement and does not cause any recognition confidence errors.

[0114] The second layer: Get all the data in the column where "12:75:49" is located, determine the column model to which all cell data belongs, and use a voting algorithm to determine whether the column mode of the column is a time column mode.

[0115] The third layer: The interactive window outlier detection method based on time series locates errors. According to the empirical value, the slope confidence radius threshold is set to 0.03. The "12:75:49" in the time column is located as an abnormal sequence, where "7" is an abnormal point and needs to be corrected.

[0116] S4: Similarity correction based on text dot matrix:

[0117] Set the similarity threshold to 0.9, measure the similarity between the dot matrix image of "7" and the dot matrix images of all characters in the candidate character set, select the one with a similarity greater than 0.9 as the candidate correction character, and select "1" as the candidate correction character with the highest similarity. Correct "12:75:49" to "12:15:49", which is judged to meet the time column pattern, and the correction is completed.

[0118] The technical features of the above embodiments may be combined in any manner. To simplify the description, not all possible combinations of the technical features in the above embodiments are described. Only preferred embodiments of the present invention are presented. While the description is relatively specific and detailed, it should not be construed as limiting the scope of the present invention. As long as there are no contradictions in the combination of these technical features, they should be considered to be within the scope of this specification.

[0119] It should be noted that those skilled in the art may make various modifications and improvements without departing from the scope of the present invention, and these modifications and improvements fall within the scope of protection of the present invention. Therefore, the scope of protection of the patent for this invention shall be based on the appended claims.

Claims

1. A method for correcting errors in recognition results of paper-based equipment operation records, characterized in that: It includes the following steps: Step S1: Scan and identify the paper-based device operation record to obtain the device operation record table and the confidence level corresponding to each character in the device operation record table; Step S2: Perform hierarchical error detection and marking on the identified device operation record table: Mark the characters with a confidence level less than the set confidence level threshold in the device operation record table as error characters; Mark the characters that do not conform to the preset column pattern in each column of the device operation record table as error characters; Take each column of the device operation record table as a time series, calculate the features of each time series, and regard the time series with features greater than the set feature threshold as an abnormal series, including the following steps: Step S201: Convert a column of characters with length n into a time series; X(t)=(x(t1),x(t2),x(t3),...,x(t n )); Where, x(t i ) is t i Characters of the moment; Step S202: Use a sliding window with length w and w < n to perform equal-length segmentation on the time series, with a sliding step size of r, and continuously slide (n - w) / r times to obtain (n - w) / r + 1 sub-time series with length w; Step S203: Calculate the confidence interval radius of the slope of each sub-time series as the feature of the sub-time series, and regard the sub-time series with the confidence interval radius greater than the set confidence interval threshold as an abnormal series; Calculate the relative distance between characters in the abnormal series, and mark the characters with the relative distance greater than the set distance threshold as error characters; Step S3: Select, from the preset candidate character set, the character with the highest similarity to the error character and conforming to the column pattern of the error character column as the corrected character to obtain the corrected device operation record table.

2. The method for correcting errors in recognition results of paper-based equipment operation records according to claim 1, characterized in that: The calculation of the confidence interval radius of the slope of each sub-time series in Step S203 includes the following steps: Step S2031: Calculate the slope of each character in the sub-time series to obtain w - 𝟏 slope values; Step S2032: Calculate the mean and variance of the slope of each sub-time series; Step S2033: Calculate the confidence interval radius of the slope of each sub-time series. The expression for calculating the confidence interval radius is: Where, d j is the radius of the confidence interval; θ j↑ is the upper confidence limit; θ j↓ is the lower confidence limit; s j is the mean of the slope; σ j is the mean square error of the slope; Z is a random variable that satisfies the normal distribution; α is the confidence level.

3. The method for correcting errors in recognition results of paper-based equipment operation records according to claim 2, characterized in that: In Step S2, by calculating the distance between each character in the abnormal series and other characters, the characters with the distance greater than the set distance threshold are regarded as error characters, including the following steps: Step S211: Calculate the reachable distance of each character in the abnormal series, and calculate the local density of each character according to the reachable distance; Step S212: Calculate the local outlier factor of each character according to the local density; Step S213: Take 1 / 2 of the maximum value of the local outlier factor as the distance threshold, and regard the characters with the local outlier factor greater than the distance threshold as error characters.

4. The method for correcting errors in recognition results of paper-based equipment operation records according to claim 3, characterized in that: The expression for calculating the reachable distance in Step S211 is: RD k (p,q)=max{k-distance(q),dist(p,q)}; Where, RD k (p,q) is the distance between character p and character q; k-distance(q) is the distance between character q and the kth character closest to character q; dist(p,q) is the Euclidean distance between character p and character q; N is the dimension of the character.

5. The method for correcting errors in paper-based equipment operation record recognition results according to claim 4, characterized in that: The expression for calculating the local density in Step S212 is: Where, LRD k (p) is the local density of character p; N k-distance (p) is the set whose distance to character p is less than k-distance(q).

6. The method for correcting errors in paper-based equipment operation record recognition results according to claim 5, characterized in that: The expression for calculating the local outlier factor in Step S213 is: In the formula, LOF(p) is the local outlier factor of character p.

7. The method for correcting errors in recognition results of paper-based equipment operation records according to claim 1, characterized in that: Calculating the similarity between the erroneous character and all characters in the candidate character set of the column to which the erroneous character belongs in step S3 includes the following steps: converting the erroneous character and all characters in the candidate character set into binary images, calculating the intersection-over-union ratio between the binary image of the erroneous character and the binary images of all characters in the candidate character set, and using the intersection-over-union ratio as the similarity between the erroneous character and the characters in the candidate character set.

8. The method for correcting errors in paper-based equipment operation record recognition results according to claim 1, characterized in that: In step S2, a corresponding regular expression is designed for each column in the equipment operation record table according to the pattern of each column character, as the column pattern of each column.

9. A system for correcting errors in recognition results of paper-based equipment operation records, applicable to the method for correcting errors in recognition results of paper-based equipment operation records according to any one of claims 1 to 8, characterized in that: It includes character recognition module, error location module and character correction module; The character recognition module converts the text on the paper equipment operation record into characters using OCR technology, and outputs the equipment operation record and the confidence level corresponding to each character in the equipment operation record; The error location module includes a confidence error location module, a column pattern error location module and a time series outlier location module; The confidence error positioning module is configured to mark characters in the equipment operation record table whose confidence is lower than a set confidence threshold as error characters; The column pattern error locating module checks whether the recognition result of each column character of the equipment operation record table conforms to a preset column pattern, and marks characters that do not conform to the column pattern of the corresponding column as error characters; The time series outlier location module treats each column of characters in the equipment operation record table as a time series, and uses an outlier detection method to identify and mark all abnormal data in the time series as erroneous characters; The character correction module calculates the similarity between the erroneous character and the characters in the candidate character set, selects the character with the highest similarity as the correction candidate, ensures that the selected character conforms to the column mode of the column to which the erroneous character belongs, and outputs a corrected equipment operation record table.

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