OCR recognition system based on big data analysis

Abstract

The present invention discloses an OCR recognition system based on big data analysis, which relates to the technical field of optical character recognition. The system includes: an environmental data acquisition module, a data preprocessing module, a big data analysis and processing module, a model selection and adjustment module, an OCR recognition module, and a feedback optimization module. The present invention improves the environmental adaptability of the OCR recognition system by using multiple sensors and complex data preprocessing technologies. The system can collect multi-dimensional data of environmental humidity, temperature, light, vibration, and tilt in real time, and remove noise and normalize processing steps through filters to improve the accuracy and reliability of the data. In the preprocessing of text image data, the system can automatically crop text areas, remove background parts, and perform adaptive image enhancement processing according to light intensity, including histogram equalization in low light and gamma correction in strong light, thereby reducing the influence of environmental factors on the OCR recognition effect.

Description

Technical Field

[0001] The present invention relates to the technical field of optical character recognition, and in particular to an OCR recognition system based on big data analysis. Background Art

[0002] With the rapid development of information technology, OCR (Optical Character Recognition) technology, as an efficient and automated text recognition method, has been widely used in multiple fields such as document scanning, image processing, and automated data entry, greatly improving the efficiency of data processing and information extraction. OCR technology can convert text in images into editable and searchable text format, providing great convenience for users.

[0003] Traditional OCR technology mainly relies on static image processing and pattern recognition algorithms, and lacks sufficient adaptability and robustness to changes in environmental factors. For example, when humidity, temperature, and light environmental conditions change significantly, traditional OCR technology may experience problems such as increased recognition error rate and decreased recognition speed. In addition, traditional OCR technology has relatively limited ability to handle noise and deformation interference factors in images, which further limits its application scope in complex environments. At the same time, traditional OCR technology often uses a single recognition model, which is difficult to flexibly adjust according to changes in environmental complexity, thus affecting its recognition effect.

[0004] Therefore, the development of an OCR recognition system based on big data analysis will open up new paths for the development and application of OCR technology. Summary of the Invention

[0005] The purpose of the present invention is to make up for the shortcomings of the existing technology and provide an OCR recognition system based on big data analysis. By integrating multiple sensors and complex data processing technologies, it realizes the real-time collection and precise processing of multi-dimensional data such as environmental humidity, temperature, light, vibration and tilt, and can intelligently select the most appropriate OCR recognition model according to the complexity of the environment. In addition, the present invention also continuously optimizes the system performance and recognition model by introducing feedback optimization, ensuring that the system can continue to adapt to various complex environments.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: an OCR recognition system based on big data analysis, the system comprising: an environmental data acquisition module, a data preprocessing module, a big data analysis and processing module, a model selection and adjustment module, an OCR recognition module, and a feedback optimization module;

[0007] Environmental data acquisition module: By placing capacitive humidity sensors, thermocouple temperature sensors, light sensors containing photodiodes and optical filters, piezoelectric vibration sensors, and MEMS three-axis tilt sensors around the OCR device, it can obtain environmental humidity, temperature, light, vibration, and tilt data in real time, while also obtaining the text image data to be recognized;

[0008] Data preprocessing module: This module uses a Butterworth low-pass filter to remove noise from various environmental data collected by the sensor and then normalizes the data. For text image data, the text area is automatically cropped and irrelevant background parts are removed. Image enhancement is performed using an adaptive method based on light intensity, including histogram equalization in low light and gamma correction in strong light. Median filtering is also used to remove salt and pepper noise and Gaussian noise in the image.

[0009] Big data analysis and processing module: Receive pre-processed sensor data, use Hadoop HDFS to store environment and recognition data, and use MySQL to manage data metadata. The environmental complexity comprehensive evaluation formula is used to quantify the complexity of the environment for OCR recognition. Let E be the environmental complexity, H be the humidity value, T be the temperature value, L be the light intensity, V be the vibration intensity, I be the tilt angle, ω1, ω2, ω3, ω 4、 ω5 is the corresponding weight, and the calculation formula is: Among them, H0, T0, and L0 are the ideal reference values ​​of humidity, temperature, and light intensity respectively. max 、H min 、T max , L max , L min are the maximum and minimum values ​​of the measurement range of humidity, temperature, and light intensity, respectively. Then, the text deformation and blurring degree prediction formula is used to predict the degree of text deformation and blurring caused by humidity and temperature changes. Let D HT is the degree of text deformation and blurring caused by humidity and temperature, α and β are the coefficients of humidity and temperature on text deformation, and the calculation formula is: D HT =α×(H-H0) 2 +β×(T-T0) 2 ;

[0010] Model selection and adjustment module: establish a model library containing multiple OCR models, and the comprehensive evaluation value of environmental complexity E is lower than the set threshold E th , the model selection and adjustment module selects a simple OCR model, and the comprehensive evaluation value of environmental complexity E exceeds the threshold E th , select a complex model based on the type and degree of environmental change;

[0011] OCR recognition module: In text recognition, the corresponding algorithm is used when performing recognition, and the environmental information and text deformation prediction and correction results are combined. At the same time, the recognition parameters are adjusted according to the characteristics of different text carriers such as paper and display screens;

[0012] Feedback optimization module: Feedback the OCR recognition results and intermediate data to the big data analysis module, evaluate the system performance through evaluation indicators, and make optimization suggestions to the big data analysis and model adjustment modules after comparing historical results and discovering problems.

[0013] Furthermore, the data preprocessing module performs histogram equalization under low light conditions and calculates the grayscale histogram of the image. Assuming that the grayscale range of the image I(x, y) is [0, L-1], where x and y are the pixel coordinates of the image, the frequency p(k) of each grayscale level k (0≤k≤L-1) is counted, and the calculation formula is: Among them, n k is the number of pixels with gray level k, M×N is the total number of pixels in the image, and the cumulative distribution function CDF is calculated as follows: Calculate the grayscale value T(k) after equalization using the formula: T(k) = round((L-1) × C(k)), where the round function rounds the result to the nearest integer. Replace the grayscale value of the pixel with grayscale value k in the original image with T(k) to obtain the image after histogram equalization.

[0014] Furthermore, in the gamma correction under strong light in the data preprocessing module, assuming that the grayscale value of the original image pixel is I(x,y), the grayscale value after gamma correction is O(x,y), the gamma value is γ and γ>0, then the gamma correction formula is:

[0015] Furthermore, the gamma value in the data preprocessing module is calculated based on the average brightness B of the image. avg To adjust the gamma value γ, the calculation formula is: γ=1+k×(B avg -B th ), where k is the proportionality coefficient, B th is the strong light judgment threshold, the average brightness B avg , let the pixel coordinates of the image be (x, y), the brightness value be I(x, y), the total number of pixels in the image be M×N, and the average brightness calculation formula be: The strong light judgment threshold is B th =350cd / m 2 .

[0016] Furthermore, the environment complexity threshold E in the model selection and adjustment module thDetermine the environment complexity comprehensive evaluation formula value E by using the environment complexity comprehensive evaluation formula, and record the recognition accuracy A of the simple OCR recognition model in each environment. The calculation formula is: where N correct is the number of correctly recognized characters, N total is the total number of characters to be recognized. Draw a curve of the relationship between the environment complexity E and the recognition accuracy A, observe the changing trend of the curve, and the environment complexity point where the recognition accuracy drops significantly is used as the threshold E th .

[0017] Furthermore, the complex OCR recognition model based on convolutional neural network in the model selection and adjustment module is designed as follows: a CNN architecture with multiple convolutional layers, pooling layers and fully connected layers is designed. A simple CNN architecture includes: a convolutional layer, which uses multiple convolution kernels to perform convolution operations on the input text image. Assuming the convolution kernel size is k×k, the step size is s, and the number of convolution kernels is n, for the input image I, the feature map F after the convolution operation is calculated as follows: Where ω is the convolution kernel weight, b is the bias term, i, j are the coordinates of the feature map, pooling layer: add maximum pooling after the convolution layer to reduce the data dimension, set the pooling window size to p×p, the step size to s, for the feature map F, the feature map P after pooling is calculated as: Fully connected layer: After several convolution and pooling layers, the feature map is flattened into a one-dimensional vector and connected to one or more fully connected layers. The number of neurons in the fully connected layer gradually decreases, and the number of nodes in the final output layer is equal to the number of text categories to be recognized.

[0018] Furthermore, the calculation formula of the simple OCR model in the model selection and adjustment module is: Where P(y|x) is the probability that the text belongs to category y given the input image feature x, n is the number of training samples, and x is the probability that the text belongs to category y. i is the feature vector of the i-th training sample, α i is the parameter related to the i-th training sample, K(x,x i ) is a custom kernel function, and the calculation formula is: K(x,x i )=(a(x·x i )+r) d , where a is a tuning parameter used to control the scaling of the kernel function, r is a constant, and d is the degree of the polynomial used to measure the difference between the input feature x and the training sample feature x i The similarity between them, b is the bias term, and C is the total number of text categories.

[0019] Furthermore, the image feature vector is adjusted by the image feature adjustment formula under the influence of the light spectrum in the OCR recognition module when the light spectrum changes. Let F L is the adjusted image feature vector, F0 is the original image feature vector, b is the light spectrum influence coefficient, which is obtained by analyzing the image features under different light spectra, S is the light spectrum feature vector, and the calculation formula is: F L =F0×(1+b×S).

[0020] Furthermore, the OCR recognition module corrects the image deformation caused by vibration and tilt through the image correction formula under the influence of vibration and tilt. Suppose I corrected is the corrected image, I is the original image, δ V is the vibration influence function, δ I is the tilt influence function, which is obtained by modeling the relationship between vibration and tilt data and image changes. The calculation formula is: corrected =I×(1-δ V (V))×(1-δ I (I)).

[0021] Compared with existing technologies, this OCR recognition system based on big data analysis has the following beneficial effects:

[0022] 1. The present invention improves the environmental adaptability of the OCR recognition system by using multiple sensors and complex data preprocessing technology. The system can collect multi-dimensional data such as environmental humidity, temperature, light, vibration and tilt in real time, and improve the accuracy and reliability of the data through noise removal and normalization processing steps through filters. In the preprocessing of text image data, it can automatically crop the text area, remove the background part, and perform adaptive image enhancement processing according to light intensity, including histogram equalization in low light and gamma correction in strong light, thereby reducing the impact of environmental factors on the OCR recognition effect and improving the accuracy and stability of recognition.

[0023] 2. The present invention realizes the intelligent selection and adjustment of OCR recognition models by constructing a model library containing multiple OCR models and introducing a comprehensive evaluation mechanism of environmental complexity. It can calculate the comprehensive evaluation value of environmental complexity based on the environmental data collected in real time, and select the most appropriate OCR recognition model for text recognition accordingly. This mechanism not only improves the recognition efficiency, but also can flexibly adjust the model for environments of different complexities, thereby improving the overall performance and adaptability of the OCR recognition system.

[0024] Other advantages, objects and features of the present invention will be described in part in the following description and, in part, will be apparent to those skilled in the art based on an examination of the following or may be learned from the practice of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort.

[0026] Figure 1 This is a flowchart of the OCR recognition system based on big data analysis;

[0027] Figure 2 This is a flowchart of the big data analysis and processing module. DETAILED DESCRIPTION

[0028] The following is a clear and complete description of the technical solutions in the embodiments of the present invention. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0029] Example 1:

[0030] Digitization of ancient books in libraries

[0031] For data collection, various sensors are arranged around the OCR equipment used for scanning ancient books. The capacitive humidity sensor detects that the indoor humidity of the library is H, the thermocouple temperature sensor displays the temperature as T, the light sensor (including a photodiode and an optical filter) measures the light intensity as L, and the piezoelectric vibration sensor and MEMS three-axis tilt sensor detect no obvious vibration. At the same time, the text image data to be recognized on the ancient book page is obtained.

[0032] Data preprocessing: The environmental data collected by the sensor is normalized after removing noise through a Butterworth low-pass filter. For ancient text image data, the text area is automatically cropped and the surrounding irrelevant background is removed. Due to the low light intensity, histogram equalization is used to enhance the image. Assume that the grayscale range of image I(x,y) is [0,L-1], where x and y are the pixel coordinates of the image. The frequency p(k) of each grayscale level k (0≤k≤L-1) is counted, and the calculation formula is: Among them, n kis the number of pixels with gray level k, M×N is the total number of pixels in the image, and the cumulative distribution function CDF is calculated as follows: The grayscale value T(k) after equalization is calculated using the formula: T(k) = round((L-1) × C(k)), where the round function rounds the result to the nearest integer. The grayscale value of the pixel with the grayscale value k in the original image is replaced by T(k) to obtain the image after histogram equalization. At the same time, median filtering is used to remove a small amount of salt and pepper noise and Gaussian noise in the image.

[0033] Big data analysis and processing: pre-processed sensor data is input, Hadoop HDFS is used to store and identify data, and MySQL is used to manage data metadata. Assume that H is the humidity value, T is the temperature value, L is the light intensity, V is the vibration intensity, I is the tilt angle, ω1, ω2, ω3, ω 4、 ω5 is the corresponding weight. The environmental complexity E is calculated according to the comprehensive evaluation formula of environmental complexity. The calculation formula is: Then, the text deformation and blurring degree prediction formula is used to predict the degree of text deformation and blurring caused by humidity and temperature changes. The calculation formula is: D HT =α×(H-H0) 2 +β×(T-T0) 2 , where α and β are the coefficients of humidity and temperature on text deformation.

[0034] Model selection and adjustment, the calculated environment complexity E and the set threshold E th Comparative environment complexity threshold E th Determine the environment complexity comprehensive evaluation formula value E by using the environment complexity comprehensive evaluation formula, and record the recognition accuracy A of the simple OCR recognition model in each environment. The calculation formula is: where N correct is the number of correctly recognized characters, N total is the total number of characters to be recognized. Draw a curve of the relationship between the environment complexity E and the recognition accuracy A, observe the changing trend of the curve, and the environment complexity point where the recognition accuracy drops significantly is used as the threshold E th If E is lower than E th , select a simple OCR recognition model that uses manual features combined with machine learning, and the comprehensive evaluation value of environmental complexity E is lower than the set threshold E th , the model selection and adjustment module selects a simple OCR model calculation formula: Where P(y|x) is the probability that the text belongs to category y given the input image feature x, n is the number of training samples, and x is the probability that the text belongs to category y. i is the feature vector of the i-th training sample, α iis the parameter related to the i-th training sample, K(x,x i ) is a custom kernel function, and the calculation formula is: K(x,x i )=(a(x·x i )+r) d , where a is a tuning parameter used to control the scaling of the kernel function, r is a constant, and d is the degree of the polynomial used to measure the difference between the input feature x and the training sample feature x i The similarity between them, b is the bias term, and C is the total number of text categories.

[0035] During the OCR recognition process, the corresponding algorithm is used when performing recognition. The change of sunlight angle in different time periods causes the light spectrum on the book to change. The image feature adjustment formula under the influence of light spectrum is used to adjust the image feature vector when the light spectrum changes. Let F L is the adjusted image feature vector, F0 is the original image feature vector, b is the light spectrum influence coefficient, which is obtained by analyzing the image features under different light spectra, S is the light spectrum feature vector, and the calculation formula is: F L =F0×(1+b×S), for the image deformation caused by vibration and tilt, the image correction formula under the influence of vibration and tilt is used to correct the image deformation caused by vibration and tilt, and I corrected is the corrected image, I is the original image, δ V is the vibration influence function, δ I is the tilt influence function, which is obtained by modeling the relationship between vibration and tilt data and image changes. The calculation formula is: corrected =I×(1-δ V (V))×(1-δ I (I)) Adjust recognition parameters based on the characteristics of ancient paper carriers, such as adjusting character segmentation and recognition thresholds according to paper color and texture.

[0036] Feedback optimization: Feedback OCR recognition results and intermediate data to the big data analysis module, evaluate system performance through evaluation indicators, compare historical results, find certain text recognition errors or decreased accuracy, and make optimization suggestions to the big data analysis and model adjustment modules, such as adjusting model parameters and improving image preprocessing methods.

[0037] In summary, in the scenario of ancient book digitization in the library, this embodiment arranges multiple sensors around the OCR device to collect environmental data and ancient book text image data, and uses the big data analysis and processing module to calculate the environmental complexity E and the text deformation and blurring degree D. HT , based on E and the set threshold E thBy comparing and selecting the appropriate OCR recognition model, the environmental information and text deformation prediction and correction results are combined in the recognition process, and the parameters are adjusted according to the characteristics of the paper carriers of ancient books. Finally, after feedback optimization, the system performance is evaluated based on the recognition results and intermediate data, and optimization suggestions are put forward. This effectively improves the accuracy and adaptability of ancient book text recognition, provides reliable technical support for the digitization of ancient books, and helps to protect and inherit cultural heritage.

[0038] Example 2:

[0039] Outdoor billboard text recognition

[0040] For data collection, various sensors are installed on the OCR device used to shoot outdoor billboards. The capacitive humidity sensor detects the outdoor humidity as H, the thermocouple temperature sensor displays the temperature as T, the light sensor measures the light intensity as L, the piezoelectric vibration sensor detects the vibration caused by the vehicle driving (vibration intensity V), and the MEMS three-axis tilt sensor detects that the device has a certain tilt (tilt angle I) due to being held or driven by the vehicle. At the same time, the image data of the text to be recognized on the billboard is obtained.

[0041] Data preprocessing: Environmental data is denoised and normalized using a Butterworth low-pass filter. For text image data, the text area is cropped and the background is removed. Due to the strong light, gamma correction is performed under strong light and the gamma value is adjusted according to the average brightness of the image. Assume that the pixel coordinates of the image are (x, y), the brightness value is I(x, y), the total number of pixels of the image is M×N, and the average brightness is B avg The calculation formula is: Correct the bright words and use median filtering to remove image noise.

[0042] Big data analysis and processing, pre-processed data is input, and Hadoop HDFS and MySQL are used to store and manage data. Assume that E is the environmental complexity, H is the humidity value, T is the temperature value, L is the light intensity, V is the vibration intensity, I is the tilt angle, ω1, ω2, ω3, ω 4、 ω5 is the corresponding weight. According to the comprehensive evaluation formula of environmental complexity, the calculation formula is: At the same time, the text deformation and blurring degree prediction formula is used to predict the degree of text deformation and blurring caused by humidity and temperature changes. Let D HT is the degree of text deformation and blurring caused by humidity and temperature, α and β are the coefficients of humidity and temperature on text deformation, and the calculation formula is: D HT =α×(H-H0) 2 +β×(T-T0) 2 .

[0043] Model selection and adjustment, based on the calculated environment complexity E and threshold E th Compare (the threshold determination method is the same as in Example 1), if E exceeds E th , choose a complex OCR recognition model, such as a model based on a convolutional neural network (CNN). The designed CNN architecture includes the convolution layer (convolution kernel size k×k, step size s, number of convolution kernels n parameters. For the input image I, the feature map F after the convolution operation is calculated as: Where ω is the convolution kernel weight, b is the bias term, i, j are the coordinates of the feature map), pooling layer (pooling window size p×p, step size s, for feature map F, the feature map after pooling And fully connected layer: flatten the feature map into a one-dimensional vector, connect it to one or more fully connected layers, the number of neurons in the fully connected layer gradually decreases, and finally the number of output layer nodes is equal to the number of text categories to be recognized.

[0044] The OCR recognition process relies on convolutional neural networks to automatically extract text features. When performing recognition, the corresponding algorithm is used to adjust the image feature vector when the light spectrum changes. For example, the change in sunlight angle at different time periods causes the light spectrum of billboards to change. The image feature adjustment formula under the influence of light spectrum is used to adjust the image feature vector. The calculation formula is: F L =F0×(1+b×S), where the light spectrum influence coefficient b is obtained by analyzing the image characteristics under different light spectra, and the light spectrum characteristic vector S is determined according to the actual light spectrum. For image deformation caused by vibration and tilt, the image correction formula under the influence of vibration and tilt is used to correct it. The calculation formula is: I corrected =I×(1-δ V (V))×(1-δ I (I)), vibration influence function δ V (V) and the tilt influence function δ I (I) Obtained by modeling the relationship between vibration and tilt data and image changes.

[0045] Feedback optimization: Recognition results and intermediate data are fed back to evaluate system performance. If recognition accuracy is found to be reduced due to complex outdoor environmental factors (such as strong light reflection and blurred text on aging billboards), optimization suggestions are made to the big data analysis and model adjustment modules, such as adding image preprocessing methods for strong light environments and adjusting CNN model parameters to adapt to the characteristics of outdoor text.

[0046] In summary, in the outdoor billboard text recognition embodiment, the sensor installed in the OCR device obtains the environment and billboard text image data. During data preprocessing, the environment data is denoised and normalized. Under strong light, the gamma value is adjusted according to the average brightness of the image to correct the image. After big data analysis and processing, E and D are calculated. HT , according to E and E th The relationship selection model and the complex model automatically extract features with the help of convolutional neural networks during recognition. The feature vector and the corrected image are adjusted based on the light spectrum changes and vibration and tilt influence formulas. Finally, feedback optimization is used to improve system performance. This embodiment adapts to complex outdoor environments, ensures the effectiveness and stability of billboard text recognition, and has important application value in urban management and commercial advertising monitoring.

[0047] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the invention can be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims, not the foregoing description, and all variations within the meaning and range of equivalents of the claims are intended to be included therein. Any reference sign in a claim should not be construed as limiting the claim to which it relates.

Claims

1. The OCR recognition system based on big data analysis is characterized by: The system includes: environmental data acquisition module, data preprocessing module, big data analysis and processing module, model selection and adjustment module, OCR recognition module and feedback optimization module; Environmental data acquisition module: By placing capacitive humidity sensors, thermocouple temperature sensors, light sensors containing photodiodes and optical filters, piezoelectric vibration sensors, and MEMS three-axis tilt sensors around the OCR device, it can obtain environmental humidity, temperature, light, vibration, and tilt data in real time, while also obtaining the text image data to be recognized; Data preprocessing module: This module uses a Butterworth low-pass filter to remove noise from various environmental data collected by the sensor and then normalizes the data. For text image data, the text area is automatically cropped and irrelevant background parts are removed. Image enhancement is performed using an adaptive method based on light intensity, including histogram equalization in low light and gamma correction in strong light. Median filtering is also used to remove salt and pepper noise and Gaussian noise in the image. Big data analysis and processing module: Receive pre-processed sensor data, use Hadoop HDFS to store environment and recognition data, and use MySQL to manage data metadata. The environmental complexity comprehensive evaluation formula is used to quantify the complexity of the environment for OCR recognition. Let E be the environmental complexity, H be the humidity value, T be the temperature value, L be the light intensity, V be the vibration intensity, I be the tilt angle, ω1, ω2, ω3, ω 4、 ω5 is the corresponding weight, and the calculation formula is: Among them, H0, T0, and L0 are the ideal reference values ​​of humidity, temperature, and light intensity respectively. max 、H min 、T max , L max , L min are the maximum and minimum values ​​of the measurement range of humidity, temperature, and light intensity, respectively. Then, the text deformation and blurring degree prediction formula is used to predict the degree of text deformation and blurring caused by humidity and temperature changes. Let D HT is the degree of text deformation and blurring caused by humidity and temperature, α and β are the coefficients of humidity and temperature on text deformation, and the calculation formula is: D HT =α×(H-H0) 2 +β×(T-T0) 2 ; Model selection and adjustment module: establish a model library containing multiple OCR models, and the comprehensive evaluation value of environmental complexity E is lower than the set threshold E th , the model selection and adjustment module selects a simple OCR model, and the comprehensive evaluation value of environmental complexity E exceeds the threshold E th , select a complex model based on the type and degree of environmental change; OCR recognition module: In text recognition, the corresponding algorithm is used when performing recognition, and the environmental information and text deformation prediction and correction results are combined. At the same time, the recognition parameters are adjusted according to the characteristics of different text carriers such as paper and display screens; Feedback optimization module: Feedback the OCR recognition results and intermediate data to the big data analysis module, evaluate the system performance through evaluation indicators, and make optimization suggestions to the big data analysis and model adjustment modules after comparing historical results and discovering problems.

2. The OCR recognition system based on big data analysis according to claim 1, characterized in that: The data preprocessing module performs histogram equalization under low light conditions and calculates the grayscale histogram of the image. Assume that the grayscale range of the image I(x,y) is [0, L-1], where x and y are the pixel coordinates of the image. The frequency p(k) of each grayscale level k (0≤k≤L-1) is calculated using the following formula: Among them, n k is the number of pixels with gray level k, M×N is the total number of pixels in the image, and the cumulative distribution function CDF is calculated as follows: Calculate the grayscale value T(k) after equalization using the formula: T(k) = round((L-1) × C(k)), where the round function rounds the result to the nearest integer. Replace the grayscale value of the pixel with grayscale value k in the original image with T(k) to obtain the image after histogram equalization.

3. The OCR recognition system based on big data analysis according to claim 1, characterized in that: For gamma correction under strong light in the data preprocessing module, assume that the grayscale value of the original image pixel is I(x,y), the grayscale value after gamma correction is O(x,y), the gamma value is γ and γ>0, then the gamma correction formula is:

4. The OCR recognition system based on big data analysis according to claim 3 is characterized in that: The gamma value in the data preprocessing module is based on the average brightness B of the image avg To adjust the gamma value γ, the calculation formula is: γ=1+k×(B avg -B th ), where k is the proportionality coefficient, B th is the strong light judgment threshold, the average brightness B avg , let the pixel coordinates of the image be (x, y), the brightness value be I(x, y), the total number of pixels in the image be M×N, and the average brightness calculation formula be: The strong light judgment threshold is B th =350cd / m 2 .

5. The OCR recognition system based on big data analysis according to claim 1, characterized in that: The environment complexity threshold E in the model selection and adjustment module th Determine the environment complexity comprehensive evaluation formula value E by using the environment complexity comprehensive evaluation formula, and record the recognition accuracy A of the simple OCR recognition model in each environment. The calculation formula is: where N correct is the number of correctly recognized characters, N total is the total number of characters to be recognized. Draw a curve of the relationship between the environment complexity E and the recognition accuracy A, observe the changing trend of the curve, and the environment complexity point where the recognition accuracy drops significantly is used as the threshold E th .

6. The OCR recognition system based on big data analysis according to claim 1, characterized in that: The complex OCR recognition model based on convolutional neural network in the model selection and adjustment module is designed as follows: a CNN architecture with multiple convolutional layers, pooling layers and fully connected layers is designed. A simple CNN architecture includes: a convolutional layer, which uses multiple convolution kernels to perform convolution operations on the input text image. Assuming the convolution kernel size is k×k, the step size is s, and the number of convolution kernels is n, for the input image I, the feature map F after the convolution operation is calculated as follows: Where ω is the convolution kernel weight, b is the bias term, i, j are the coordinates of the feature map, pooling layer: add maximum pooling after the convolution layer to reduce the data dimension, set the pooling window size to p×p, the step size to s, for the feature map F, the feature map P after pooling is calculated as: Fully connected layer: After several convolution and pooling layers, the feature map is flattened into a one-dimensional vector and connected to one or more fully connected layers. The number of neurons in the fully connected layer gradually decreases, and the number of nodes in the final output layer is equal to the number of text categories to be recognized.

7. The OCR recognition system based on big data analysis according to claim 1, characterized in that: The calculation formula of the simple OCR model in the model selection and adjustment module is: Where P(y|x) is the probability that the text belongs to category y given the input image feature x, n is the number of training samples, and x is the probability that the text belongs to category y. i is the feature vector of the i-th training sample, α i is the parameter related to the i-th training sample, K(x,x i ) is a custom kernel function, and the calculation formula is: K(x,x i )=(a(x·x i )+r) d , where a is a tuning parameter used to control the scaling of the kernel function, r is a constant, and d is the degree of the polynomial used to measure the difference between the input feature x and the training sample feature x i The similarity between them, b is the bias term, and C is the total number of text categories.

8. The OCR recognition system based on big data analysis according to claim 1, characterized in that: The OCR recognition module uses the image feature adjustment formula under the influence of the light spectrum to adjust the image feature vector when the light spectrum changes. Let F L is the adjusted image feature vector, F0 is the original image feature vector, b is the light spectrum influence coefficient, which is obtained by analyzing the image features under different light spectra, S is the light spectrum feature vector, and the calculation formula is: F L =F0×(1+b×S).

9. The OCR recognition system based on big data analysis according to claim 1, characterized in that: The OCR recognition module corrects the image deformation caused by vibration and tilt through the image correction formula under the influence of vibration and tilt. Let I corrected is the corrected image, I is the original image, δ V is the vibration influence function, δ I is the tilt influence function, which is obtained by modeling the relationship between vibration and tilt data and image changes. The calculation formula is: corrected =I×(1-δ V (V))×(1-δ I (I)).

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