Using few shot learning on recognition system for character image in industrial processes

Abstract

An artificial intelligence optical character image recognition system and method, using few shot learning on recognition system for character image in industrial processes, mainly including: preparing two or more identical neural network architecture units, inputting similar or different character images respectively, and comparing the calculation results to see if the weights are similar. If the similarity reaches the set standard value, they are classified as the same type of character, otherwise different. Through such procedures, training samples in the storage unit are gradually divided into settings of character sets with different contextual meanings, becoming a complete AI OCR system. It can increase training sample data by comparing characters, without increasing the training set. Simultaneously, it can improve the flexibility of recognizing test characters.

Claims

1. Using few shot learning on recognition system for character image in industrial processes, mainly relating to: First, using the control unit to cut out the range of each character image to be classified, and inputting the signal to two or more sets of the same neural network architecture unit prepared, with each group of neural network architecture having the same weight parameter, then matching with the control unit to input similar or different character images into the two or more sets of identical neural network architecture units, so the two or more sets of identical neural network architecture units performing deep calculations; After the result being calculated, the input signal being used in the comparing unit to confirm whether the weights of the comparison operation results are similar, if the similarity reaching the standard value set in the comparing unit, the signal being output to the storage unit and classified as the same type of characters; otherwise the signal being output to the storage unit and classified as a different type of characters; Through this method, the training samples in the storage unit 4 gradually being divided into the settings of different contextual character sets.

2. As shown in the AI artificial intelligence text image recognition system and method in claim 1, the image capturing device mainly including: The control unit, connecting to two or more sets of identical neural network architecture units respectively, can cut out each character image range to be classified; Two or more sets of identical neural network architecture unit, with each group of neural network having the same weight parameter, able to receive similar or different character image input by the control unit with the signals connected, perform in-depth calculations with result signals being output, connected, and input to the comparing unit; Comparing unit, receiving the signals of two or more sets of identical neural network architecture unit after deep calculation, to confirm whether the weights of the comparison calculation results are similar, and outputting the signals into the storage unit; The storage unit, receiving the signal output, after the comparing unit finishing comparing. If the similarity is as high as the standard value set in the comparing unit, the signal is output into the storage unit and classified as the same type of characters, otherwise the signal is output into the storage unit and classified as different types of text. In this way, the training samples in the storage unit are gradually divided into the settings of different textual text sets.

Description

DESCRIPTION OF DRAWINGS

[0009] FIG. 1 is a block diagram of the present invention.

[0010] FIG. 2 is the main flow chart of the method of the present invention.

DETAILED DESCRIPTION OF INVENTION

[0011] The main purpose of the present invention, using few shot learning on recognition system for character image in industrial processes, is to improve the accuracy of judgments by detecting character codes on materials of different sizes in the industry, offering the actual value for industrial testing and document use, so it has a wider range of applicability.

[0012] Another purpose of the present invention, using few shot learning on recognition system for character image in industrial processes, is that, apart from the actual value for industrial detection and document use, if it is necessary to increase the text classification set, it is also easy to continue training from the existing models, thus reducing the complete effect requirements required by the advantage of the cost of maintaining the character recognition system.

[0013] In order to achieve the above and other objectives, the present invention, using few shot learning on recognition system for character image in industrial processes, is suitable for industrial character code detection on different materials and of different sizes.

[0014] The implementation method of the present invention, using few shot learning on recognition system for character image in industrial processes, is to first intercept each character's image range, and then prepare two or more sets of identical neural network architecture unit, with the same weight parameters of each group of neural network architecture units. Next, input similar or different character image respectively. After the calculation, the comparing unit compares whether the calculation result weights are similar. If the similarity is as high as the standard value set in the comparing unit, the character image will be output into the storage unit and classified as the same type of characters; otherwise, it will be output into the storage unit and classified as a different type of characters. Through this method, the training samples in the storage unit are gradually divided into settings of character sets with different contextual meanings.

[0015] Therefore, the present invention, using few shot learning on recognition system for character image in industrial processes, is a complete AI character image recognition system and method developed from the method of comparing whether the weights of the calculation results are similar. If the similarity is as high as the set standard value, it will be classified as the same type of characters, otherwise different. Through this method, the training samples in the storage unit are gradually divided into settings of character sets with different contextual meanings. For character detection, without increasing the training set, the training sample data can be increased through comparison at first, and the flexibility in recognizing test characters can be increased simultaneously. One set of training model can correspond to different fonts and different handwriting samples, while the requirements for text background are also reduced at the same time. The model can compare text features by itself in the process of feature extraction, and exclude background features, so as to improve the practical industrial detection requirements and the accuracy of judgments, improving the accuracy of character code recognition, thus reducing the cost of industrial applications. In addition, if it is necessary to increase the character classification set, it can also be easily modified from the existing model, which reduces the cost of maintaining the character recognition system and enhances the actual value advantage of industrial testing and document use.

Embodiment

[0016] The following is the embodiment of combining specific objective examples with the present invention. Those familiar with the art can easily understand the other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific examples. Based on different viewpoints and applications, various details in this patent specification can also be modified and changed in various ways without departing from the spirit of the present invention.

[0017] First of all, please refer to FIGS. 1 and 2 with the remaining images. The present invention, using few shot learning on recognition system for character image in industrial processes, mainly relates to: First, the control unit 1 intercepts the range of each character image to be classified, and it inputs the signal into two or more sets of identical neural network architecture unit 2. Each group of neural network architecture has the same weight parameter. Then, it is matched with the control unit 1 to input similar or different character image into the two or more sets of identical neural network architecture units 2, so that the two or more sets of identical neural network architecture units 2 performs deep calculations. After the result is calculated, the input signal is used in the comparing unit 3 to confirm whether the weights of the comparing operation results are similar. If the similarity is as high as the standard value set in the comparing unit 3, the result signal will be output into the storage unit 4 and classified as the same type of characters; otherwise the result signal will be output to the storage unit 4 and be classified as a different type of characters. Through this method, the training samples in the storage unit 4 are gradually divided into settings of character sets with different contextual meanings.

[0018] Please further refer to FIG. 1 and FIG. 2 with the remaining images. The present invention, using few shot learning on recognition system for character image in industrial processes, is based on the concept of this method. This AI character image recognition system mainly includes: Control unit 1, connected to two or more sets of identical neural network architecture units 2 respectively, can intercept each character image range to be classified.

[0019] Two or more sets of identical neural network architecture unit 2. Each group of neural network has the same weight parameter, and receives similar or different character image input by the control unit 1 with the signals jointed, so that these two or more sets of identical neural network architecture unit 2 perform in-depth calculations. After calculating the results, the signals will be output and jointed into the comparing unit 3.

[0020] Comparing unit 3, jointed from receiving the signals of two or more sets of identical neural network architecture unit 2 after deep calculation, to confirm whether the weights of the comparison calculation results are similar, and outputting the signals and jointed into the storage unit 4.

[0021] The storage unit 4, jointed from receives the signal output, after the comparing unit 3 finished comparing. If the similarity is as high as the standard value set in the comparing unit 3, the signal is output into the storage unit 4 and classified as the same type of characters, otherwise the signal is output into the storage unit 4 and classified as a different type of characters. In this way, the training samples in the storage unit 4 are gradually divided into settings of character sets with different contextual meanings.

[0022] Please further refer to FIG. 1 and FIG. 2 with the remaining images. The present invention, using few shot learning on recognition system for character image in industrial processes, is a complete AI character image recognition system and method developed from the method of comparing whether the weights of the calculation results are similar. If the similarity is as high as the set standard value, it will be classified as the same type of characters, otherwise different. Through this method, the training samples in the storage unit are gradually divided into settings of character sets with different contextual meanings. For character detection, without increasing the training set, the training sample data can be increased through comparison at first, and the flexibility in recognizing test characters can be increased simultaneously. One set of training model can correspond to different fonts and different handwriting samples, while the requirements for text background are also reduced at the same time. The model can compare text features by itself in the process of feature extraction, and exclude background features, so as to improve the practical industrial detection requirements and the accuracy of judgments, improving the accuracy of character code recognition, thus reducing the cost of industrial applications. In addition, if it is necessary to increase the character classification set, it can also be easily modified from the existing model, which reduces the cost of maintaining the character recognition system, having the advantage of enhancing the actual value of industrial testing and document use to meet the need for effects in use, thus becoming the effective creative factor of the present invention.