Text detection method, system and equipment based on multi-receptive field depth characteristics and medium

Abstract

The invention discloses a text detection method, system and device based on multi-receptive field depth characteristics and a medium, and the method comprises the steps: obtaining a text detection database, and taking the text detection database as a network training database; building a multi-receptive field depth network model; inputting a natural scene text picture and corresponding textbox coordinate true value data in the network training database into a multi-receptive field depth network model for training; calculating an image mask for segmentation through the trained multi-receptive field depth network model to obtain a segmentation result, and converting the segmentation region into a regression textbox coordinate; and counting the textbox size of the network training database, designing a textbox filtering condition, and screening out a target textbox according to the textbox filtering condition. The method fully utilizes the feature learning capability and classification performance of the deep network model, combines the characteristics of image segmentation, has the characteristics of high detection accuracy, high recall rate, strong robustness and the like, and has agood text detection effect in a natural scene.

Description

technical field [0001] The invention relates to a text detection method, system, equipment and medium based on multi-receptive field depth features, belonging to the field of image text analysis. Background technique [0002] With the development of computer vision technology, the application of image understanding technology is becoming more and more extensive. As an information-dense point of an image, text information is the key to image understanding. Text localization is an important step in extracting key information from images. However, due to the variety of backgrounds and the uncertainty of size and direction, the positioning of Chinese text in natural scene images faces the following challenges: 1) changes in text format and aspect ratio of text lines; 2) text boxes with multiple directions 3) The size of the text box varies greatly; 4) The confusion caused by the similarity between the text and the background. [0003] There are currently two main methods for ...

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Problems solved by technology

[0006] At present, the disadvantage of implementing text boxes based on image segmentation methods is that the use of single-dimensional convolutional features will cause position deviation in text box regression, or the text box cannot completely cover the text area. The problem of low accuracy and recall of text box detection with multi-scale and complex background

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