Low-Resolution Chinese Character Recognition of Vehicle License Plate Based on ALBP and Gabor Filters

2009 ◽  
Author(s):  
Ye Wang ◽  
Honggang Zhang ◽  
Xu Fang ◽  
Jun Guo
Keyword(s):  
Character Recognition ◽  
Chinese Character ◽  
Gabor Filters ◽  
License Plate ◽  
Low Resolution ◽  
Chinese Character Recognition

A Feature Extracting Method for Chinese Character Recognition of License Plate Based on Elastic Mesh and Wavelet Transform

2013 ◽  
Vol 760-762 ◽  
pp. 1452-1456
Author(s):  
Chao Zheng ◽  
Hua Yang ◽  
Xing Yang ◽  
Chao Chao Huang ◽  
Xiao Di Wu
Keyword(s):  
Wavelet Transform ◽  
Character Recognition ◽  
Chinese Character ◽  
Wavelet Coefficient ◽  
Difficult Problem ◽  
License Plate ◽  
Structure Information ◽  
Feature Extracting ◽  
Chinese Character Recognition ◽  
Character Feature

Low-resolution Chinese character recognition of license plate is always a difficult problem. For solving it, we must think about the distinctiveness of character feature and the counting speed of method simultaneously. In this paper, we proposed a simple and effective feature extraction algorithm. First, extract the statistical feature of Chinese character based on decomposing stroke with wavelet transform. Second, apply Elastic Mesh Algorithm into extracting wavelet coefficient of decomposing stroke to get the structure information of Chinese character. The experimental results show the method is robust against low quality Chinese characters, such as skew, fuzzy, glue, distorted character, and easy to be used in actual projects with simple advantage.

scholarly journals Effect of Pixel’s Spatial Characteristics on Recognition of Isolated Pixelized Chinese Character

2015 ◽  
Vol 9 (1) ◽  
pp. 234-239
Author(s):  
Kun Yang ◽  
Shuang Liu ◽  
Hong Wang ◽  
Wei Liu ◽  
Yaowei Wu
Keyword(s):  
High Resolution ◽  
Character Recognition ◽  
Chinese Character ◽  
Recognition Accuracy ◽  
Chinese Characters ◽  
Low Resolution ◽  
Spatial Characteristics ◽  
Head Mounted Display ◽  
Chinese Character Recognition ◽  
Pixel Array

The influence of pixel’s spatial characteristics on recognition of isolated Chinese character was investigated using simulated prosthestic vision. The accuracy of Chinese character recognition with 4 kinds of pixel number (6*6, 8*8, 10*10, and 12*12 pixel array) and 3 kinds of pixel shape (Square, Dot and Gaussian) and different pixel spacing were tested through head-mounted display (HMD). A captured image of Chinese characters in font style of Hei were pixelized with Square, Dot and Gaussian pixel. Results showed that pixel number was the most important factor which could affect the recognition of isolated pixelized Chinese Chartars and the accuracy of recognition increased with the addition of pixel number. 10*10 pixel array could provide enough information for people to recognize an isolated Chinese character. At low resolution (6*6 and 8*8 pixel array), there were little difference of recognition accuracy between different pixel shape and different pixel spacing. While as for high resolution (10*10 and 12*12 pixel array), the fluctuation of pixel shape and pixel spacing could not affect the performance of recognition of isolated pixelized Chinese Character.

A Method of License Plate Chinese Character Recognition Based on Image Quality

2012 ◽  
Vol 424-425 ◽  
pp. 309-313
Author(s):  
Yu Feng Chen ◽  
Gang Yin
Keyword(s):  
Image Quality ◽  
Character Recognition ◽  
Minimum Distance ◽  
Chinese Character ◽  
Recognition Rate ◽  
Experimental Results ◽  
License Plate ◽  
Chinese Character Recognition ◽  
Recognition Result ◽  
Fuzzy Degree

This paper presents a method of license plate Chinese character recognition based on image quality for improving the recognition rate of low-resolution characters in license plates. Each subset of license plate Chinese character images with the same fuzzy degree are used to construct a corresponding PCA subspace, then the projection residuals distance between the Chinese character to be recognized and each subspace in the specific subspaces which are selected by character’s fuzzy degree is calculated. The class which corresponds to the subspace with the minimum distance is the recognition result. The experimental results show that the proposed method has higher recognition rate and faster recognition speed comparing to other ones.

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