Handwritten Farsi/Arabic Word Recognition

This paper presents a novel holistic handwritten Farsi /Arabic word recognition scheme in situation where we face with word rotation and scale change. Image words features are extracted by exploiting rotation and scale invariance characteristics of M-Band packet wavelet transform performed on polar...

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Bibliographic Details
Main Authors: Broumandnia, A., Shanbehzadeh, J., Nourani, M.
Format: Conference Proceeding
Language:English
Subjects:
Character recognition
Discrete wavelet transforms
Face recognition
Farsi/Arabic Handwritings Recognition
Feature extraction
Handwriting recognition
Pattern Recognition
Shape
Text recognition
Wavelet coefficients
Wavelet packets
Wavelet Transform
Wavelet transforms
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Summary:This paper presents a novel holistic handwritten Farsi /Arabic word recognition scheme in situation where we face with word rotation and scale change. Image words features are extracted by exploiting rotation and scale invariance characteristics of M-Band packet wavelet transform performed on polar transform version of images of handwritten Farsi/Arabic words. The extracted features construct a feature vector for each word image. This vector is employed in recognition phase by finding the similar words based on the least Mahalanobis distance of feature vectors. This scheme is robust against rotation and scaling. Experimental results, obtained from testing different handwritten texts with various orientations and scales, show that proposed scheme outperforms Fourier-wavelet and Zernike moments algorithms. The robustness of new scheme has been tested with images corrupted by Gaussian noise and compared with similar schemes. Experimental results show that the accuracy of our algorithm reaches 95.8 percents.
ISSN:2161-5322
2161-5330
DOI:10.1109/AICCSA.2007.370719