Complex optical text sequence identification system based on convolution and recurrent neural network

Abstract

The invention relates to the image and text identification field, and specifically relates to a complex optical text sequence identification system based on convolution and recurrent neural network. The complex optical text sequence identification system includes an image and text input module, a sliding sampling module, a CNN and an RNN, wherein the image and text input module is a scanner, a digital camera or an image and text storage module; the sliding sampling module performs sliding sampling of an image and text sequence to be identified, and inputs the sampling sub images in the CNN; the CNN extracts the characteristics and outputs the characteristics to the RNN; and the RNN successively identifies the front part of each character, the back part of each character, numbers, letters, punctuation, or blank according to the CNN input signal and the output data of the CNN for the last moment. The complex optical text sequence identification system based on convolution and recurrent neural network can realize complex image and text sequence identification, can overcome the cutting problem, and can significantly improve the identification efficiency and accuracy for the complex image and text sequence.

Description

technical field [0001] The invention relates to the field of image text recognition, in particular to complex optical text sequence recognition based on convolutional and recursive neural networks Background technique [0002] With the development of society, there is a large demand for the digitization of ancient books, documents, bills, business cards and other paper media. The digitization here is not limited to "photographic" using scanners or cameras, but more importantly, the Files are converted into readable and editable documents for storage. This process requires image text recognition on scanned pictures, and traditional image text recognition is optical text recognition (OCR). [0003] Conventional OCR methods include image segmentation, feature extraction, single character recognition and other processing processes, wherein image segmentation includes a large number of image preprocessing processes, such as tilt correction, background denoising, and single char...

AI Technical Summary

Problems solved by technology

[0003] Conventional OCR methods include image segmentation, feature extraction, single character recognition and other processing processes, wherein image segmentation includes a large number of image preprocessing processes, such as tilt correction, background denoising, and single character extraction; these processes The process is not only cumbersome and time-consuming, but also may cause the image to lose a lot of available information; and when the image to be recognized contains a string of multiple characters, the traditional OCR method needs to divide the original string into several small images containing a single character for separation. Recognition, and the most commonly used method for text segmentation is the projection method, that is, after binarizing the image text, find the dividing line between the two texts through vertical projection, and separate the text according to the dividing line. This method The main problems are: when the image text sequence to be recognized contains background noise, character distortion, character bonding, etc., it will cause difficulty in text segmentation

Especially when the image text sequence to be recognized is mixed with Chinese characters, letters, numbers, and symbols with left and right radicals, or characters in half-width and full-width formats are mixed in the image text sequence to be recognized, the character size and gaps exist due to the difference in format. The difference is that the single character in the image text sequence to be recognized cannot be accurately segmented through the simple projection method

Once there is a problem with segmentation, it is difficult to obtain accurate recognition results.

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