Text Recognition Method Fused with Multi-layer Feature Enhanced Attention Mechanism

Abstract

The present invention relates to the technical field of optical character recognition in computer vision, and provides a text recognition method that integrates multi-layer features to enhance the attention mechanism. The method includes: selecting training pictures; extracting picture features; constructing a feature fusion matrix and fusing multi-layer features ; Use associated features for feature fusion to enhance feature performance; sequence modeling for fused features; probability prediction for sequence modeling features; in the training phase, use backpropagation to update the parameter weights of the network model , to obtain a standard network model that can be used for text recognition; in the test phase, the picture to be recognized is input into the trained network model, and the model recognizes and outputs the text in the picture. The present invention improves the expressive ability of the features by mapping the features extracted from each level of the neural network to each other, thereby improving the accuracy of character recognition.

Description

technical field [0001] The invention relates to the technical field of optical character recognition in computer vision, in particular to a character recognition method that integrates multi-layer features to enhance attention mechanism. Background technique [0002] In the era of the mobile Internet, a large amount of picture data can be sent and received every day, many of which contain text information, and it is particularly important to be able to accurately extract the text information in the pictures. People may need to convert the manuscripts taken by mobile phones into electronic versions, or they may need to save the text in the pictures they usually see, and so on. As the number of pictures increases, the text in the pictures also increases, and it has gradually become a new trend to be able to accurately recognize the text in the pictures. Text recognition is mainly to process the part of the picture with the text area, convert the color information in the pictu...

AI Technical Summary

Problems solved by technology

The advantage of this method is that the recognition accuracy is high; but due to its complex network structure, the recognition speed is slow

[0006] Although with the development of deep learning, the accuracy of text recognition under the action of neural network is getting higher and higher, but because the effect of recognition is closely related to the structure of the network, different network structures have different effects on the features extracted from the same picture. are quite different, and therefore give different results

Especially when the network structure is particularly complex and the number of network layers is deep, because the extracted features are too abstract, the accuracy of the final prediction result is actually lower than other methods.

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