Random sample generation method for recognition of complex character

Abstract

The invention relates to the field of image recognition, and especially relates to a random sample generation method for the recognition of a complex character. In the recognition of the complex character, a large number of samples, which are generated by a random sample generator and comprise a to-be-recognized image noise model and a distortion characteristic model are employed through the analysis of the reason of the character complexity on the basis of a standard character similar to a to-be-recognized character. A training sample automatically generated by the random sample generator comprises various types of complex noises and distortions, and can meet the recognition demands of various types of complex characters. The above random sample is enabled to serve as a training sample in a depth neural network, thereby solving a problem that a training neural network needs a large amount of manual marking for character recognition, enabling the automatic recognition of complex characters and images to be more simple and easier, and remarkably saving the related labor cost.

Description

technical field [0001] The invention relates to the field of image recognition, in particular to a random sample generation method for complex character recognition. Background technique [0002] Image recognition is of great significance in the field of intelligent recognition. With the advancement of technology and the development of society, the demand for automatic recognition of text in pictures is also increasing rapidly. Traditional Optical Character Recognition (OCR) systems are often used to identify documents scanned using optical devices, such as digitized ancient books, business cards, invoices, forms, etc. Usually this type of scanned document has a relatively high resolution and contrast, and the printed fonts are generally relatively single and regular, making it easier to extract a single text for recognition. Therefore, the core of this type of document recognition is to eliminate noise. There are many ways to eliminate noise: for example, use Gaussian for ...

AI Technical Summary

Problems solved by technology

By analyzing the reasons for the complexity of the text, a large number of training samples containing various noise and distortion features that can be used by the deep neural network are automatically generated, which solves the problem of requiring a large amount of human labeling when using the deep neural network to recognize text in the prior art , significantly saving labor costs; improving the efficiency of identification

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