1D Wavelet Transform of Projection Profiles for Isolated Handwritten Malayalam Character Recognition

2007 ◽  
Author(s):  
Renju John ◽  
G. Raju ◽  
D.S. Guru
Keyword(s):  
Wavelet Transform ◽  
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.

Wavelet-Based Recognition of Handwritten Characters Using Artificial Neural Network

Biometrics ◽  
2017 ◽  
pp. 1043-1060
Author(s):  
D. K. Patel ◽  
T. Som ◽  
M. K. Singh
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Character Recognition ◽  
Process Time ◽  
Discrete Wavelet ◽  
Training Time ◽  
Handwritten Character ◽  
Artificial Neural

In the present chapter, the widely common problem of handwritten character recognition has been tackled with multiresolution technique using discrete wavelet transform and artificial neural networks. The technique has been tested and found to be more accurate and economic in respect of the recognition process time of the system. Features of the handwritten character images are extracted by discrete wavelet transform used with appropriate level of multiresolution technique, then the artificial neural networks is trained by extracted features. The unknown input handwritten character images are recognized by trained artificial neural networks system. The proposed method provides good recognition accuracy for handwritten characters with less training time, less no. of samples and less no. of iterations.

THINNING CHARACTER USING MODULUS MINIMA OF WAVELET TRANSFORM

2006 ◽  
Vol 20 (03) ◽  
pp. 361-375 ◽  
Author(s):  
XINGE YOU ◽  
QIUHUI CHEN ◽  
BIN FANG ◽  
YUAN YAN TANG
Keyword(s):  
Wavelet Transform ◽  
Character Recognition ◽  
Medial Axis ◽  
Maximum Modulus ◽  
Processing Technique ◽  
Coarse Scale ◽  
Distance Transforms ◽  
Local Modulus ◽  
Gray Level Image ◽  
Fine Structures

An essential step in character recognition is to extract the skeleton characteristics of the character. In this paper, an efficient algorithm is proposed to extract visually satisfactory skeleton from printed and handwritten characters, which overcomes fundamental shortcomings of our previous skeletonization technique based on the maximum modulus symmetry of wavelet transform (WT). The proposed method is motivated from some desirable properties of the WT with constructed wavelet functions: namely, the local modulus minima of the WT are scale-independent at different level scales and are located at the medial axis of the symmetrical contours of character stroke. Thus the modulus minima of the WT are computed as the intrinsic skeletons of character strokes. To achieve faster implementation, a multiscale processing technique is employed. Thus major structures of the skeleton are extracted using the coarse scale, while fine structures are extracted using the fine scale. We have tested the algorithm on handwritten and printed character images. Experimental results show that the proposed algorithm is applicable to not only binary image but also gray-level image where it can be impractical to use other skeletonization techniques, such as thinning and distance transforms. Further, it can effectively remove unwanted artifacts and branches from the extracted skeletons at the intersections and junctions of character strokes and is robust against noises while most existing methods perform poorly.

Alternative representations of an image via the 2D wavelet transform: application to character recognition

1995 ◽  
Author(s):  
Jean-Pierre Antoine ◽  
Pierre Vandergheynst ◽  
Karim Bouyoucef ◽  
Romain Murenzi
Keyword(s):  
Wavelet Transform ◽  
Character Recognition
Sign in / Sign up

Export Citation Format

Share Document