Natural scene text recognition method based on cross attention mechanism

Abstract

The invention discloses a natural scene text recognition method based on a cross attention mechanism, and the method comprises the steps: data obtaining: downloading a sample picture in a natural scene, and synthesizing the picture into a training set through employing a public code; wherein stretching operation is conducted on the sizes of all the training sample pictures, the size of the processed sample pictures is 32 * 100, the height-width ratio is kept consistent with that of an original picture, and the insufficient parts are filled with black edges; label manufacturing: a supervised method is adopted to train an identification model, so that each row of text pictures has corresponding text information; training a network: inputting the prepared training picture data and labels intoa cross attention network for training, wherein the cross attention network is composed of a vertical attention network and a horizontal attention network; inputting test data into the trained network, and finally obtaining an identification result and predicting the confidence coefficient of each character. The method is high in recognition accuracy and strong in robustness, and has good recognition performance for texts with irregular shapes.

Description

technical field

[0001] The invention belongs to the technical field of pattern recognition and artificial intelligence, in particular to a natural scene text recognition method based on a cross-attention mechanism. Background technique

[0002] With the rapid development of computer technology, artificial intelligence technology is gradually changing our lives, making our lives more convenient and efficient. The recent rapid development of GPU and other hardware technologies has also made the practical application of deep neural networks possible.

[0003] In real life, we cannot do without text. Most of the information that humans obtain visually is carried by words. Whether in the past or in the future, human beings will rely heavily on obtaining information from text, and the crucial step in obtaining text information is to correctly recognize text. It is easy for humans to recognize text from a picture, but it is not an easy task for computers. If a computer is neede...

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