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A Method of Scene Image Text Detection Based on Discriminative Dictionary Learning and Sparse Representation

A dictionary learning and sparse representation technology, applied in character and pattern recognition, instruments, computing, etc., can solve problems such as difficulty in detecting text in scene images, and achieve the effect of improving accuracy.

Active Publication Date: 2019-07-05
云南联合视觉科技有限公司
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0009] The technical problem to be solved by the present invention is to provide a method for scene image text detection based on discriminative dictionary learning and sparse representation, so as to solve the problem that the prior art is difficult to study scene image text detection. The scene image text detection of the present invention The method can provide strong support for upper-level applications such as image and video understanding and retrieval in different application scenarios

Method used

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  • A Method of Scene Image Text Detection Based on Discriminative Dictionary Learning and Sparse Representation
  • A Method of Scene Image Text Detection Based on Discriminative Dictionary Learning and Sparse Representation
  • A Method of Scene Image Text Detection Based on Discriminative Dictionary Learning and Sparse Representation

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Embodiment 1

[0059] Embodiment 1: as Figure 1-7 As shown, a method of scene image text detection based on discriminative dictionary learning and sparse representation, first uses the training data and the proposed discriminative dictionary learning method to train and learn two dictionaries: the text dictionary and the background dictionary, and then sequentially merge the text Dictionary and background dictionary; then the sparse representation coefficients of the text and background corresponding to the image to be detected are calculated from the merged dictionary, the image to be detected, and the sparse representation method; finally, the learned dictionary corresponds to the calculated image to be detected Sparse representation coefficients to reconstruct the text in the image to be detected; use heuristic rules to process the text area in the reconstructed text image to detect the candidate text area in the image to be detected;

[0060] The specific steps are:

[0061] Step1, fir...

Embodiment 2

[0095] Embodiment 2: as Figure 1-7 shown, will be attached figure 2 The text in the source image to be detected in is detected. attached figure 2 It is a scene image with a complex background. The overall image is seriously polluted by light, and the geometric features of the background are very similar to those of the text. It is difficult to accurately detect the text in the image with traditional methods. The following describes the detection figure 2 TextArea steps in:

[0096] Step1, first construct the training samples of text and background;

[0097] Step1.1. Collect text images and background images from the Internet, wherein the text images only contain text without background texture, and the background images do not contain text.

[0098] Step1.2, collect the text image and background image data in Step1.1 in the form of sliding window, each window (n×n) collects data as a column vector (n 2 ×1) (hereinafter collectively referred to as atoms, n is the size ...

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Abstract

The invention relates to a method for detecting text in scene pictures based on discriminative dictionary learning and sparse representation, and belongs to the technical field of digital image processing. First, use the training data and the proposed discriminative dictionary learning method to train and learn two dictionaries: the text dictionary and the background dictionary, and then sequentially merge the text dictionary and the background dictionary; then the merged dictionary, the image to be detected and the method of sparse representation Calculate the sparse representation coefficients of the text and background corresponding to the image to be detected; finally, reconstruct the text in the image to be detected from the learned dictionary and the calculated sparse representation coefficient corresponding to the image to be detected; use heuristic rules to reconstruct The text area in the text image is processed to detect the candidate text area in the image to be detected; the method of distinguishing dictionary learning and scene image text detection with sparse representation proposed by the present invention can greatly improve the accuracy of text recognition.

Description

technical field [0001] The invention relates to a method for detecting text in scene pictures based on discriminative dictionary learning and sparse representation, and belongs to the technical field of digital image processing. Background technique [0002] Since entering the 21st century, the Internet industry has developed rapidly, coupled with the vigorous development of smart phones in recent years, the digital information on PC and mobile terminals is growing rapidly. Digital images and videos are just one of the main elements of today's digital world. Digital images and videos often contain a large number of text areas, and these text information are important clues to understand the meaning of the images and videos. How to extract text information from complex natural scene images will have extraordinary significance for image understanding and image retrieval. Therefore, the research on text positioning technology in scene images has attracted many scholars at home ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/20G06K9/62
CPCG06V10/22G06F18/214
Inventor 李华锋刘舒萍汤宏颖余正涛
Owner 云南联合视觉科技有限公司
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