character detection and recognition method for Chinese historical literature dense texts

Abstract

The invention discloses a character detection and recognition method for Chinese historical literature dense texts, which comprises the following steps of: (1) making data acquisition: acquiring historical literature images, and manually labeling the historical literature images; (2) pre-processing data: performing column segmentation on the vertical projection of the historical literature images,and cutting the vertical text in the historical literature according to columns; (3) constructing and pre-training a convolutional neural network for single-row text recognition; (4) constructing a convolutional neural network for performing character detection on the single-row text, sharing shallow parameters with the convolutional neural network for performing single-row text recognition, andperforming training at the same time; and enabling the text detection convolutional neural network to identify the text information provided by the convolutional neural network by using the text, andfinely adjusting the detection position, so that the single text position of the dense text in the historical literature can be accurately detected. According to the invention, the convolutional neural network is adopted to realize text recognition, the guidance information of the text recognition classifier is fully utilized, and the detection effect can be more accurate.

Description

technical field [0001] The invention relates to the technical fields of pattern recognition and artificial intelligence, in particular to a text detection and recognition method for dense texts of Chinese historical documents. Background technique [0002] A large number of historical documents are the precious heritage left by past civilizations. The most effective way to interpret and protect these historical documents is to digitize them, including identifying and preserving the words and symbols in them. In recent years, deep learning algorithms have made a series of breakthroughs in the field of computer vision, and general object detection algorithms and scene text detection algorithms have been greatly improved. However, for dense text in Chinese historical documents, general object The detection algorithm and scene text detection framework are not ideal, and the detection effect affects the further text recognition of historical documents. Therefore, accurate detecti...

AI Technical Summary

Problems solved by technology

In recent years, deep learning algorithms have made a series of breakthroughs in the field of computer vision, and general object detection algorithms and scene text detection algorithms have been greatly improved. However, for dense text in Chinese historical documents, general object The detection algorithm and scene text detection framework are not ideal, and the detection effect affects the further text recognition of historical documents. Therefore, accurate detection of dense text in historical documents is of great significance to the digitization of historical documents. great significance

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