Text detection and recognition method based on convolutional neural network and in natural scene

A convolutional neural network and natural scene technology, applied in the field of text detection and recognition in natural scenes, can solve the problems of increased difficulty in parameter optimization, high calculation cost, high complexity, etc., to avoid character segmentation process and high recognition accuracy , to avoid the effect of identifying errors

Inactive Publication Date: 2017-09-26
NORTHWESTERN POLYTECHNICAL UNIV
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Problems solved by technology

This type of method has an obvious distinction between the text detection stage and the recognition stage. The advantages are: the overall process is from coarse to fine, and many background areas are filtered out during the detection and removal of non-text areas, which greatly reduces the The calculation cost is reduced; the disadvantage is that when ir

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  • Text detection and recognition method based on convolutional neural network and in natural scene
  • Text detection and recognition method based on convolutional neural network and in natural scene
  • Text detection and recognition method based on convolutional neural network and in natural scene

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[0016] The present invention will be further described below in conjunction with the accompanying drawings and embodiments, and the present invention includes but not limited to the following embodiments.

[0017] Such as figure 1 As shown, the steps of a method for text detection and recognition in a natural scene based on a convolutional neural network of the present invention are as follows:

[0018] 1. Prepare the data set

[0019] The picture sets of different scenes with text annotations in advance are divided into training sample sets and test sample sets. Generally, the number of pictures in the training sample set accounts for more than 80% of the total pictures. The so-called annotation with text mainly refers to that the image contains text and has corresponding text borders and text content annotations. In order to detect and recognize text in natural scenes more effectively, the more diverse the text fonts, colors, and layouts in the pictures of the training sam...

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Abstract

The invention provides a text detection and recognition method based on a convolutional neural network and in a natural scene. The method includes: training a quick area convolutional neural network to acquire a network for text detection in the natural scene to perform text detection in the natural scene; collecting vocabularies to acquire a dictionary; learning features of text vocabularies to train a convolutional neural network classifier, and classifying input areas to realize text recognition. By utilizing the method, good text detection and recognition results can be acquired under the background of different complex degrees and different text fonts and colors.

Description

technical field [0001] The invention belongs to the technical field of computer vision, object detection and recognition, and specifically relates to a text detection and recognition method in a natural scene based on a fast regional convolutional neural network and a convolutional neural network. Background technique [0002] Compared with the visual cues of traditional low-level features, text rich in clear high-level semantic features provides useful information in many visual tasks, so detecting and recognizing text in natural scenes is an important technology in computer vision . Usually, due to complex natural scenes, variable text distribution, fonts, colors, uneven lighting effects, and camera resolution, it is difficult to detect and recognize text in natural scenes. Currently, there are two types of text detection and recognition methods: [0003] The first is a method with a clear distinction between text detection and recognition. The method generally includes...

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Application Information

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IPC IPC(8): G06F17/30G06N3/04
CPCG06F16/374G06F16/353G06N3/04
Inventor 王琦李学龙李红丽
Owner NORTHWESTERN POLYTECHNICAL UNIV
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