Complex image and text sequence identification method based on CNN-RNN

Abstract

The invention relates to the image and text identification field, and specifically relates to a complex image and text sequence identification method based on CNN-RNN. The complex image and text sequence identification method includes the steps: utilizing a sliding sampling box to perform sliding sampling on an image and text sequence to be identified; extracting the characteristics from the sub images obtained through sampling by means of a CNN and outputting the characteristics to an RNN, wherein the RNN successively identifies the front part of each character, the back part of each character, numbers, letters, punctuation, or blank according to the input signal; and successively recording and integrating the identification results for the RNN at each moment and acquiring the complete identification result, wherein the input signal for each moment for the RNN also includes the output signal of a recursion neural network for the last moment and the vector data converted from the recursion neural network identification result for the last moment. The complex image and text sequence identification method based on CNN-RNN can overcome the cutting problem of a complex image and text sequence and the problem that the identification result relies on a language model, thus significantly improving the identification efficiency and accuracy for images and text.

Description

technical field [0001] The present invention relates to the field of image character recognition, in particular to a complex image word sequence recognition method based on CNN-RNN. 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 character extraction;...

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 There are two main problems: 1. When the image text to be recognized contains background noise, character distortion, and character bonding, etc., it makes it difficult to segment the text

Especially when the image text to be recognized is mixed with Chinese characters, letters, numbers, and symbols with left and right radicals, or the image text to be recognized is mixed with half-width and full-width characters, the size and gap of the characters are different due to the difference in format. The single character in the image text 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.

2. The method of dividing the string into sub-pictures containing a single character for separate recognition does not make full use of the dependencies between words and words in natural language. Although additional language models can be used to optimize and supplement the recognition results, but considering The construction process of language model and recognizer is independent of each other, and the optimization supplement of this method is locally limited

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