Image detection method and device

Abstract

The embodiment of the invention discloses an image detection method and device. The image detection method comprises the steps that an image to be detected is obtained, and a maximally stable extremal region (MSER) is extracted from the image to be detected, wherein the MSER is a communication region, and a text region in the image to be detected is obtained by filtering the MSER. The MSER is extracted to serve as a candidate region by extracting the MSER from the image to be detected in a communication region dividing mode, then the extracted MSER is filtered and screened, and finally the text region in the image to be detected is obtained. Region division is conductive to decrease of calculation amount and improvement of the detection efficiency, meanwhile the interference of an image background can be decreased by extracting the MSER, and the accuracy rate during detection of images having complicated backgrounds can be improved.

Description

technical field [0001] The present invention relates to the technical field of image processing, in particular to an image detection method and device. Background technique [0002] With the maturity and popularization of digital camera equipment, people have been able to record all aspects of the real world under different perspectives very conveniently and quickly. As a human language, visual text has a special and irreplaceable position in human activities. Text detection in natural scenes is one of the important research topics of computer vision and pattern recognition technology in the field of target detection and recognition. The purpose of this technology is to accurately detect text information in the captured natural scene images. It has broad application prospects in natural scene understanding and analysis, robot-assisted navigation, video retrieval, assisted reading for the blind, and text translation. [0003] Currently, natural scene text detection methods ...

AI Technical Summary

Problems solved by technology

[0004] The method based on the sliding window refers to sliding a multi-scale window from left to right and from top to bottom in the image, and classifying the image in the sliding window to determine whether it is a text area. In order to be able to detect all Text area, this method usually requires a large number of sliding windows, resulting in increased computational complexity and cannot meet real-time requirements

[0005] The method based on connected regions refers to the similarity clustering of pixels according to the inherent properties of the text, such as color, texture, stroke width, etc., to generate a large number of connected regions, and perform characteristics on the connected regions (such as text height, width and spacing, etc.) extraction, filtering non-text areas, so as to complete the text detection, compared with the method based on the sliding window, the calculation amount of this method is relatively reduced, but it has high requirements for the extraction of connected regions, that is, the extracted connected regions To include all text areas, and it is difficult to deal effectively with complex backgrounds

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