Complicated background image and character division method

Abstract

The invention discloses a complicated background image and character division method. The complicated background image and character division method mainly comprises the following steps of: reading an image, reading a character area of the read image, extracting a bottom layer color feature and a bottom layer texture feature of the character area, fusing the extracted bottom layer color feature and the bottom layer texture feature to obtain a bottom layer local feature, extracting a label layer global feature of the character area, fusing the bottom layer local feature and the label layer global feature of the character area to obtain feature vectors of all the pixels in the character area, training the feature vectors of all the pixels in the character area to obtain a first-stage division classifier, carrying out first-stage character division by using the trained classifier, carrying out connected element calibration on the result of the first-stage division, extracting connected element features to carry out second-stage character division, and outputting the result of the character division. The complicated background image and character division method is capable of improving the division correctness of the characters in complicated background images and has certain generality and practicability.

Description

technical field

[0001] The invention relates to an image segmentation technology in the field of image processing, in particular to a text segmentation method for a complex background image. Background technique

[0002] With the wide application of image acquisition equipment such as digital cameras, cameras, and ultra-high-speed scanners, the information in images has attracted more and more attention. However, it is still difficult for computers to understand the content of images. The text embedded in the image is an important way of expressing the semantic content of the image. If these words can be automatically extracted and recognized, the machine can automatically understand the content of the picture and classify the picture, and then use these words to mark and retrieve the picture with the help of mature text retrieval technology, so as to provide content-based image and video retrieval. Provide a way. However, the text embedded in the image usually has a compl...

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