Linguistic text detecting and positioning system, and linguistic text detecting and positioning method based on system

Abstract

The invention provides a linguistic text detecting and positioning system, and a linguistic text detecting and positioning method based on the system. The system includes a feature extraction network for extracting bottom-layer CNN features out of a to-be-detected image to obtain a plurality of different feature maps; at least three area suggestion networks for conducting the dichotomy and the boundary frame regression of the above different feature maps respectively and generating text candidate areas according to text-contained feature maps; a transition region for connecting a plurality of text candidate areas and generating an area convolution feature map according to the above text-contained feature maps and the text candidate areas; and a text detection network for generating the text area boundary frame bias information according to the above area convolution feature map, conducting the non-maximum suppression on the bias information and the filtering operation on non-reasonable areas, and generating the predicted text area boundary frame coordinate information in an image coordinate space.

Description

technical field [0001] The invention relates to the field of image detection information, in particular to a language text detection and positioning system and a language text detection and positioning method using the system. Background technique [0002] Linguistic text in images often contains valuable information, and this information is exploited in many content-based image and image applications, such as content-based web image search, image information retrieval, and automatic text analysis and recognition. Traditional methods for language text localization in complex backgrounds can be roughly divided into three categories: methods based on sliding windows, methods based on connected components, and hybrid methods. [0003] A typical method such as the method based on connected components using MSERs (Maximum Stable Extremum Region), which uses a multi-stage method to locate language text information: first extract the MSERs regions of the three channels of the image...

AI Technical Summary

Problems solved by technology

This type of method has a good effect in the field of general object detection, but due to the complex and changeable language text shape, it is often in a small and long position in the image, so it is difficult to directly apply this type of method for language text detection

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