Natural scene text recognition method based on geometric prior and knowledge graph

Abstract

A natural scene text recognition method based on geometric prior and a knowledge graph comprises the following steps: acquiring a field scene text image, detecting and cutting out a text line image, and performing feature extraction and columnar deformation correction through a deformation correction model based on geometric prior; perceiving each character of the corrected image through a visual identification module based on an attention mechanism to obtain aligned visual texture features of a character level; then, scene domain knowledge is introduced through a global semantic reasoning module based on a domain knowledge graph, context information is sensed, and high-level semantic features are coded; and finally, integrating the output of the visual and semantic modules to obtain a text recognition result. The method can be migrated and applied to different field-oriented natural scene text recognition of automatic control instruments, equipment manufacturing, numerical control machine tools, automobile manufacturing, rail transit and the like, the problem that the recognition accuracy is not high due to cylindrical text deformation and lack of related dictionaries in natural scenes in a traditional text recognition technology is solved, and more accurate recognition of field texts is achieved.

Description

technical field [0001] The invention belongs to the technical field of artificial intelligence, and relates to feature extraction and text recognition of field-oriented text images such as electrical equipment, which is used for intelligent text recognition of natural scenes, and is a natural scene text recognition based on geometric priori and knowledge maps. method. Background technique [0002] With the needs of technological development and economic development, work in various industrial scenarios has also begun to be upgraded and optimized intelligently and digitally. A typical example is that the front-line substation operation and maintenance personnel need to engage in a large number of regular inspections of equipment, which is time-consuming and labor-intensive, greatly reducing work efficiency. Moreover, due to the large number of substations in the jurisdiction, it is often difficult for operation and maintenance personnel to find defects at the first time. Th...

AI Technical Summary

Problems solved by technology

[0006] The problem to be solved by the present invention is to solve the problem that the traditional text recognition technology may have a low recognition accuracy rate in practical application scenarios such as substation terminal row inspections, and the purpose is to comprehensively and accurately recognize the field-oriented field scene text

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