On Image Analysis by the Methods of Moments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Principles of multivariate analysis: a user's perspective
Principles of multivariate analysis: a user's perspective
A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Classification of Rotated and Scaled Textured Images Using Gaussian Markov Random Field Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of moment-based techniques for unoccluded object representation and recognition
CVGIP: Graphical Models and Image Processing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Log-Polar Wavelet Energy Signatures for Rotation and Scale Invariant Texture Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Extraction of Shift Invariant Wavelet Features for Classification of Images with Different Sizes
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
Spectral features for Arabic word recognition
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 06
Wavelet Theory and Its Application to Pattern Recognition
Wavelet Theory and Its Application to Pattern Recognition
A translation-invariant wavelet representation algorithm withapplications
IEEE Transactions on Signal Processing
Design of efficient M-band coders with linear-phase andperfect-reconstruction properties
IEEE Transactions on Signal Processing
Theory of regular M-band wavelet bases
IEEE Transactions on Signal Processing
Entropy-based algorithms for best basis selection
IEEE Transactions on Information Theory - Part 2
Wavelet descriptor of planar curves: theory and applications
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
A translation- and scale-invariant adaptive wavelet transform
IEEE Transactions on Image Processing
Document image segmentation using wavelet scale-space features
IEEE Transactions on Circuits and Systems for Video Technology
IEEE Transactions on Neural Networks
Novel Framework for Selecting the Optimal Feature Vector from Large Feature Spaces
ICIAR '09 Proceedings of the 6th International Conference on Image Analysis and Recognition
Binary segmentation with neural validation for cursive handwriting recognition
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Binary segmentation algorithm for English cursive handwriting recognition
Pattern Recognition
Offline arabic handwritten text recognition: A Survey
ACM Computing Surveys (CSUR)
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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.