Multi-category text detection system and bill form detection method based on system

A technology of text detection and detection method, which is applied in the direction of neural learning method, character recognition, character and pattern recognition, etc. It can solve the problems of inaccurate bounding box, influence of results, uneven heat map, etc., and achieve high detection accuracy and prediction The effect of simple process and strong generalization ability

Active Publication Date: 2020-10-02
ZHEJIANG UNIV CITY COLLEGE +1
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AI Technical Summary

Problems solved by technology

The disadvantage of this type of method is pixel aggregation: since pixel aggregation is performed based on the information of each pixel, the final result will be largely affected by a single pixel
First of all, the heat map needs to determine the category of each pixel according to the set threshold, so the heat map of the smallest scale is not uniform, and different thresholds will get different areas of seed points, which leads to large scales in the process of gradual expansion. Account for a small possibility, resulting in an inaccurate bounding box
Secondly, limited by the expressive ability of the small-scale heat map, when the text is close to each other, the heat map will merge, so it is easy to merge incorrectly; when the text is too long, the heat map will appear in a thin line shape, which is very prone to splitting

Method used

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  • Multi-category text detection system and bill form detection method based on system
  • Multi-category text detection system and bill form detection method based on system
  • Multi-category text detection system and bill form detection method based on system

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Embodiment 1

[0051] The first embodiment of the present invention provides a multi-category text detection system, which includes: an image acquisition module for acquiring an image of the bill form to be detected, and a feature extraction for extracting multi-scale features of the bill form image to be detected Module, which is used to fuse the multi-scale features extracted by the feature extraction module and pass them to the pyramid bridge module of the decoding module, and is used to decode the fusion features through three branches to generate classification maps, center point heat maps and distance map decoding modules respectively .

[0052] The feature extraction module is also called the backbone network, which is responsible for transforming the original image into high-dimensional features, and is composed of a classic convolutional neural network structure; the pyramid bridging module is to output each layer of the backbone network through the PA module, and convert the feature...

Embodiment 2

[0059] The second embodiment of the present invention provides a bill form detection method, which is based on the multi-category text detection system in the first embodiment above, as figure 2 As shown, it includes the following steps:

[0060] In the first step, the preprocessed pictures are input into the multi-category text detection system to generate a center point map, a category map and a distance map respectively.

[0061] As a preferred implementation, the preprocessing includes scaling the picture to a fixed size (512*512) and normalizing it, and then inputting it into the multi-category text detection system. The multi-category text detection system has three outputs, which are category map (size 64*256*256), center point probability map (size 2*256*256), and distance map (size 8*256*256).

[0062] In the second step, center point positioning, the center point is found in the center point map based on the extreme point detection method, so as to determine the po...

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Abstract

The invention provides a bill form detection method, which comprises the following steps of: inputting a pre-processed bill form picture into a multi-category text detection system, and respectively generating a central point graph, a category graph and a distance graph; searching a central point in the central point graph based on an extreme point detection method so as to determine the positionof a semantically independent field; determining the size of each semantic independent field in the distance map based on the searched central point, thereby determining a candidate box; and based onthe candidate box, determining the category of the candidate box in the category graph by adopting a voting mechanism. Compared with the prior art, the method has the following beneficial effects: based on the idea of center point detection, post-processing of non-maximum suppression (NMS) can be avoided, so that the process is simplified, the prediction process is simple, the speed is high, the detection accuracy is high, and the robustness is good.

Description

technical field [0001] The invention relates to the technical field of intelligent detection, in particular to a multi-category text detection system and a bill form detection method based on the system. Background technique [0002] Text recognition is a long-standing problem, and with the rise of deep learning, most related tasks have been well solved. However, there is a special type of data whose text is organically combined based on semantic information, called bill form-like data, which needs to extract the required information from the visual and semantic levels. [0003] Bills and form-like data play an important role in daily life. As one of the original accounting documents, it is the carrier for recording the content of economic activities and an important tool for financial management. There are many types of form-like data in different forms, which can be roughly divided into bill-type data and form-type data. For bill data, the most common ones are value-adde...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06V30/413G06V30/414G06V30/10G06N3/045G06F18/253Y02D10/00
Inventor 魏金岭王剑强丁续旭孙怡王昌胜魏弋力
Owner ZHEJIANG UNIV CITY COLLEGE
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