A text detection and recognition method for boarding pass information verification

Abstract

The invention relates to a character detection and recognition method for boarding pass information verification, and belongs to the field of computer vision. The method comprises the following steps:S1, reading a boarding pass image, and obtaining a boarding pass test image and a training image; S2, positioning each text block through a text line detection method of a multi-task full convolutional neural network model based on a fuzzy region; S3, through text recognition model learning based on a CTC and a self-attention mechanism, realizing recognition of a text line, namely a positioned text block; S4, establishing a boarding pass common text library so as to learn an n-gram language model, and assisting in optimizing a text line recognition result. The boarding pass character information is automatically detected and recognized, Chinese and English mixed text line recognition is achieved, and more comprehensive personal information is obtained.

Description

technical field [0001] The invention belongs to the field of computer vision and relates to a text detection and recognition method for boarding pass information verification. Background technique [0002] Existing text detection and text recognition technologies are not effective in boarding pass text recognition. Since the boarding pass text is not arranged neatly and texts will overlap, the current text detection technology has not proposed an effective solution to this problem. solution. [0003] In addition, the general text recognition algorithms are mostly researched based on English text, but there are too many types of Chinese text, about six to seven thousand types, and the existing deep learning network is not suitable for Chinese recognition. At present, the boarding pass is verified and cleared by scanning the barcode. The barcode only contains part of the information (such as flight number, seat number, origin, date, etc.), while the passenger's name, arrival ...

AI Technical Summary

Problems solved by technology

However, there are also many common problems in the field of computer vision, such as changes in illumination, deformation, angle and occlusion, which have a great impact on detection performance.

Therefore, it is difficult to make a detection technology that can be applied to various scenarios, and the current text line detection algorithm cannot solve this text superposition situation

[0005] Traditional deep learning algorithms based on text line recognition are all based on lstm (Long Short-Term Memory, long-term short-term memory network) network for model training and testing. Since lstm has a good context memory function, it is used in the traditional field of English recognition Extensive, but when the number of text categories is too large, the efficiency of lstm is low, and the Chinese text category is much larger than the English alphabet category

Images