Persian/arabic handwritten word recognition using M-band packet wavelet transform

The extraction of rotation and scale invariant features is an essential problem in document image analysis. This paper proposes an effective rotation and scale invariant holistic handwritten word recognition scheme. This approach utilizes M-band packet wavelet transform to extract feature vector of Farsi word image. The global and local features extracted are exploited in recognition of limited-size lexicon of handwritten words. The rotation and scale invariant feature of a word image involves applying a polar transform to eliminate rotation and scale effects, but this produces M-row shifted polar image, which is passed to a row shift invariant M-band wavelet packet transform to eliminate the row shift effects. The output wavelet coefficients are rotation and scale invariant. For each subband of these wavelet coefficients a set of local energy features are computed and we extract feature vectors from the subbands of wavelet coefficients. The proposed polar M-band wavelet features have been tested by employing Mahalanobis algorithm to classify a set of distinct natural handwriting Farsi words. We compared the proposed scheme with two well-known rotation invariant methods; Fourier-wavelet and Zernike moments. The experimental results show that the proposed algorithm improves the recognition rate about 12 percents.

[1]  Roland T. Chin,et al.  On Image Analysis by the Methods of Moments , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Wojtek J. Krzanowski,et al.  Principles of multivariate analysis : a user's perspective. oxford , 1988 .

[3]  Mausumi Acharyya,et al.  Extraction of Features Using M-Band Wavelet Packet Frame and Their Neuro-Fuzzy Evaluation for Multitexture Segmentation , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Mandyam D. Srinath,et al.  Invariant character recognition with Zernike and orthogonal Fourier-Mellin moments , 2002, Pattern Recognit..

[5]  Ehud Rivlin,et al.  Offline cursive script word recognition – a survey , 1999, International Journal on Document Analysis and Recognition.

[6]  Yuan Yan Tang,et al.  A novel approach to optical character recognition based on ring-projection-wavelet-fractal signatures , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[7]  Chi-Man Pun,et al.  Log-Polar Wavelet Energy Signatures for Rotation and Scale Invariant Texture Classification , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Po-Cheng Chen,et al.  Invariant pattern recognition by moment fourier descriptor , 1994, Pattern Recognit..

[10]  Hong Yan,et al.  Newspaper layout analysis incorporating connected component separation , 2004, Image Vis. Comput..

[11]  Thomas W. Parks,et al.  A translation-invariant wavelet representation algorithm with applications , 1996, IEEE Trans. Signal Process..

[12]  Tianxu Zhang,et al.  A translation- and scale-invariant adaptive wavelet transform , 2000, IEEE Trans. Image Process..

[13]  C.-C. Jay Kuo,et al.  Wavelet descriptor of planar curves: theory and applications , 1996, IEEE Trans. Image Process..

[14]  W. F. Clocksin,et al.  Spectral features for Arabic word recognition , 2000, 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100).

[15]  M. M. Leung,et al.  Scale and rotation invariant texture classification , 1992, [1992] Conference Record of the Twenty-Sixth Asilomar Conference on Signals, Systems & Computers.

[16]  Peter N. Heller,et al.  Theory of regular M-band wavelet bases , 1993, IEEE Trans. Signal Process..

[17]  Wen-Rong Wu,et al.  Correction To "rotation And Gray-scale Transform-invariant Texture Classification Using Spiral Resampling, Subband Decomposition, And Hidden Markov Model" , 1996, IEEE Trans. Image Process..

[18]  Mausumi Acharyya,et al.  M-Band Wavelets: Application to Texture Segmentation for Real Life Image Analysis , 2003, Int. J. Wavelets Multiresolution Inf. Process..

[19]  Alireza Khotanzad,et al.  Classification of invariant image representations using a neural network , 1990, IEEE Trans. Acoust. Speech Signal Process..

[20]  Oktay Alkin,et al.  Design of efficient M-band coders with linear-phase and perfect-reconstruction properties , 1995, IEEE Trans. Signal Process..

[21]  Richard J. Prokop,et al.  A survey of moment-based techniques for unoccluded object representation and recognition , 1992, CVGIP Graph. Model. Image Process..

[22]  Yuan Yan Tang,et al.  Wavelet Theory and Its Application to Pattern Recognition , 2000, Series in Machine Perception and Artificial Intelligence.

[23]  Andrew F. Laine,et al.  Wavelet descriptors for multiresolution recognition of handprinted characters , 1995, Pattern Recognit..

[24]  Alireza Khotanzad,et al.  Rotation invariant image recognition using features selected via a systematic method , 1990, Pattern Recognition.

[25]  F. S. Cohen,et al.  Classification of Rotated and Scaled Textured Images Using Gaussian Markov Random Field Models , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[26]  S. Mallat.  A wavelet tour of signal processing , 1998 .

[27]  Chi-Man Pun,et al.  Extraction of shift invariant wavelet features for classification of images with different sizes , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[28]  Guangyi Chen,et al.  Invariant Fourier-wavelet descriptor for pattern recognition , 1999, Pattern Recognit..

[29]  Lev S. Sadovnik,et al.  Scale-, rotation-, and shift-invariant wavelet transforms , 1994, Defense, Security, and Sensing.

[30]  Hervé Carfantan,et al.  Time-invariant orthonormal wavelet representations , 1996, IEEE Trans. Signal Process..

[31]  Israel Cohen,et al.  Orthonormal shift-invariant wavelet packet decomposition and representation , 1997, Signal Process..

[32]  Jane You,et al.  Classification and segmentation of rotated and scaled textured images using texture "tuned" masks , 1993, Pattern Recognit..

[33]  Ronald R. Coifman,et al.  Entropy-based algorithms for best basis selection , 1992, IEEE Trans. Inf. Theory.

[34]  Paulo J. G. Lisboa,et al.  Translation, rotation, and scale invariant pattern recognition by high-order neural networks and moment classifiers , 1992, IEEE Trans. Neural Networks.

[35]  Mausumi Acharyya,et al.  Document image segmentation using wavelet scale-space features , 2002, IEEE Trans. Circuits Syst. Video Technol..

[36]  Brian Everitt,et al.  Principles of Multivariate Analysis , 2001 .

[37]  A. Kundu,et al.  Rotation and Gray Scale Transform Invariant Texture Identification using Wavelet Decomposition and Hidden Markov Model , 1994, IEEE Trans. Pattern Anal. Mach. Intell..