Method and apparatus for generating text line classifier

Patent metadata

Field	Value
Publication number	US-10146994-B2
Application number	US-201615080047-A
Country	US
Kind code	B2
Filing date	Mar 24, 2016
Priority date	Mar 25, 2015
Publication date	Dec 4, 2018
Grant date	Dec 4, 2018

Abstract

A method of generating a text line classifier including generating text line samples by use of a present terminal system font reservoir. The method also includes extracting features from the text line samples and pre-stored marked-up samples. The method further includes training models by use of the extracted features to generate a text line classifier for recognizing text regions. With the system font reservoir being utilized for generating text line samples, the generated text line classifiers can target different scenes or different requirements for text region recognition with a high degree of applicability and wide application in addition to ease of implementation. Together with the combinational use of the marked up samples for extracting features from the text line samples, the generated text line classifiers provide for enhanced classification efficiency and accuracy.

First claim

What is claimed is:

1. A method of generating a text line classifier for recognizing text regions in an image, the method comprising:
generating a plurality of lines of text characters, a number of text characters in a line of text characters being variations of text characters in a font reservoir, generating the plurality of lines of text characters to include:
selecting a plurality of text characters from the font reservoir;
varying an aspect of the plurality of text characters to form a plurality of character samples;
randomly arranging a number of character samples from the plurality of character samples to form a line of character samples; and
varying an aspect of the line of character samples to form a line of text characters; and

generating a plurality of pre-stored marked-up samples;
extracting a plurality of features from the plurality of lines of text characters and the plurality of pre-stored marked-up samples; and
training a plurality of models using the plurality of extracted features to generate the text line classifier.

2. The method of claim 1 , wherein training the plurality of models includes:
generating type models corresponding to types of the line of text characters based on the extracted features; and
assigning weights to the type models based on the pre-stored marked-up samples to generate the text line classifier.

3. A method of recognizing text regions in an image, the method comprising:
selecting a plurality of characters from a font reservoir;
generating a line of text based on the plurality of characters, generating the line of text to include:
modifying the plurality of characters to form a plurality of modified characters; and
arranging a number of modified characters of the plurality of modified characters to form a line of modified characters;

extracting a plurality of features from the line of text;
representing the plurality of features extracted from the line of text as a first vector;
training a model utilizing the first vector to obtain a trained model;
detecting an image to be recognized;
determining a second vector from the image;
inputting the second vector into the trained model, the trained model generating a score;
determining that the image to be recognized is a text region if the score is greater than a pre-determined threshold; and
determining that the image to be recognized is a non-text region if the score is less than the pre-determined threshold.

4. A method of generating a text line classifier, the method comprising:
selecting a plurality of text characters from a font reservoir;
varying an aspect of the plurality of text characters to form a plurality of character samples;
randomly arranging a number of character samples from the plurality of character samples to form a line of character samples;
varying an aspect of the line of character samples to form a line of text characters; and
extracting from the line of text characters one or more of a gradient orientation histogram feature, a gradient magnitude histogram feature, a pixel histogram feature, and a pixel histogram change feature.

5. The method of claim 4 , wherein:
a number of text characters of the plurality of text characters differ only in that the number of text characters has a different font; and
the font reservoir includes Asian characters.

6. The method of claim 4 , wherein:
the text characters in the line of text characters have a same size, a same rotation angle, and a same font; and
more than half of the text characters in the line of text characters are commonly used characters.

7. The method of claim 4 , wherein extracting includes:
obtaining continuous regions of the line of text characters; and
extracting features of the continuous regions.

8. The method of claim 4 , further comprising, generating a model corresponding to a type of the line of text characters based on the extracted features.

9. A non-transitory computer-readable storage medium having embedded therein program instructions, which when executed by one or more processors of a device, causes the device to execute a process that generates a text line classifier for recognizing text regions in an image, the process comprising:
generating a plurality of lines of text characters, a number of text characters in a line of text characters being variations of text characters in a font reservoir, generating the plurality of lines of text characters to include:
selecting a plurality of text characters from the font reservoir;
varying an aspect of the plurality of text characters to form a plurality of character samples;
randomly arranging a number of character samples from the plurality of character samples to form a line of character samples; and
varying an aspect of the line of character samples to form a line of text characters; and

generating a plurality of pre-stored marked-up samples;
extracting a plurality of features from the plurality of lines of text characters and the pre-stored marked-up samples; and
training a plurality of models using the plurality of extracted features to generate the text line classifier.

10. The non-transitory computer-readable storage medium of claim 9 , wherein training the plurality of models includes:
generating type models corresponding to types of the line of text characters based on the extracted features; and
assigning weights to the type models based on the pre-stored marked-up samples to generate the text line classifier.

11. A non-transitory computer-readable storage medium having embedded therein program instructions, which when executed by one or more processors of a device, causes the device to execute a process that recognizes text regions in an image, the process comprising:
selecting a plurality of characters from a font reservoir;
generating a line of text based on the plurality of characters, generating the line of text to include:
modifying the plurality of characters to form a plurality of modified characters; and
arranging a number of modified characters of the plurality of modified characters to form a line of modified characters;

extracting a plurality of features from the line of text;
representing the plurality of features extracted from the line of text as a first vector;
training a model utilizing the first vector to obtain a trained model;
detecting an image to be recognized;
determining a second vector from the image;
inputting the second vector into the trained model, the trained model generating a score;
determining that the image to be recognized is a text region if the score is greater than a pre-determined threshold; and
determining that the image to be recognized is a non-text region if the score is less than the pre-determined threshold.

Assignees

• Alibaba Group Holding Ltd

Inventors

• Jin Xuan
• Wang Tianzhou
• Xue Qin

Classifications

• G06V30/1914 Primary

  Determining representative reference patterns, e.g. averaging or distorting patterns; Generating dictionaries, e.g. user dictionaries · CPC title

• G06V30/413

  Classification of content, e.g. text, photographs or tables · CPC title

• G06F18/28

  Determining representative reference patterns, e.g. by averaging or distorting; Generating dictionaries · CPC title

• G06V30/293

  of characters other than Kanji, Hiragana or Katakana · CPC title

• G06V30/2445

  Alphabet recognition, e.g. Latin, Kanji or Katakana · CPC title