A natural image character recognition method and system based on deep neural network

Abstract

The invention discloses a natural image character recognition method and system based on a deep neural network. The method includes the steps of image collection and fusion, character positioning, character recognition and rationality judgment; the system includes an image collection and fusion unit, a character detection unit, The character recognition unit and the rationality judgment unit; the character detection unit automatically locates the character area in the image through the character detector obtained through training; the character recognition unit first divides the characters into three categories: Chinese, English and numbers through the multi-interface CNN constructed, Then recognize the characters of the corresponding category; in the rationality judgment unit, the output result of the recognition unit is fused with the prediction result of the character-based LSTM model to improve the rationality of Chinese continuous character recognition. The natural image character recognition method and system can intelligently recognize characters in natural images. The core processing model is composed of a deep neural network, which can simulate the human brain to the greatest extent, and has high robustness and recognition accuracy.

Description

technical field [0001] The invention belongs to the technical field of target character recognition, and more specifically relates to a natural image character recognition method and system based on a deep neural network. Background technique [0002] With the continuous development of modern science and technology and the widespread popularization of the Internet, digital image information resources have also shown exponential growth, and a large amount of image information needs to be manually recorded in work and life. Therefore, how to quickly and accurately recognize the text information that needs to be recorded in the natural image directly through the computer has become an urgent problem to be solved. [0003] The current Optical Character Recognition (OCR) model is mainly divided into two parts: character segmentation and character recognition. However, traditional character segmentation methods need to classify positive and negative samples through the selection o...

AI Technical Summary

Problems solved by technology

[0003] The current Optical Character Recognition (OCR) model is mainly divided into two parts: character segmentation and character recognition. However, traditional character segmentation methods need to classify positive and negative samples through the selection of sliding windows and a large number of SVM calculations. These methods Usually the amount of calculation involved is very large, and it is impossible to complete the online test in real time

On the other hand, traditional character recognition methods solve the problem of feature extraction by manually selecting text features, and such artificially selected features usually have errors in the accuracy and comprehensiveness of features. Not only that, the pixel information of natural images Missing or lighting problems will also lead to a decrease in recognition accuracy

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