Character identifying method and character identifying system

Abstract

An embodiment of the invention provides a character identifying method and system. The method includes collecting an original image of a nature scene; performing pre-treatment on the original image; performing OCR layout analysis on the original image subjected to the pre-treatment and obtaining a plurality of pixel matrixes; performing characteristic extraction on the pixel matrixes by adopting a CNN (Convolutional Neural Network) and obtaining a plurality of characteristic patterns; performing character identification on the characteristic patterns by adopting an LSTM (Long Short Term Memory) provided with an Attention Model and obtaining a character sequence, wherein a forget gate of the LSTM provided with the Attention Model is replaced with the Attention Model. According to the invention, by utilizing the LSTM algorithm provided with the Attention Model, the characteristic sequence extracted by using the CNN algorithm is identified as the corresponding character sequence, so that required text information is obtained and operation parameters are reduced. At the same time, through control of different influence on current characters by different context content, information in long term memory can be transmitted to the current characters perfectly, so that character identification accuracy is improved.

Description

technical field

[0001] The invention relates to the technical field of character recognition, in particular to a character recognition method and a character recognition system. Background technique

[0002] Text recognition technology is widely used in virtual reality, human-computer interaction, bill recognition, industrial automation and other fields. For example, computers can analyze roadside signs to obtain current location or other driving information to help unmanned vehicles plan their driving routes reasonably.

[0003] The traditional optical character recognition (Optical Character Recognition, OCR) technology is mainly oriented to document images with better quality, which assumes that the input image uses a standardized print, has a clean background, and has a high resolution. However, in natural scenes, due to the variety of image background environments, uneven image acquisition equipment, variable fonts, rich layout, etc., the traditional OCR technology has...

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