A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Off-Line Cursive Script Word Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the representation of operators in bases of compactly supported wavelets
SIAM Journal on Numerical Analysis
A Lexicon Driven Approach to Handwritten Word Recognition for Real-Time Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Role of Holistic Paradigms in Handwritten Word Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Offline General Handwritten Word Recognition Using an Approximate BEAM Matching Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Use of Lexicon Density in Evaluating Word Recognizers
IEEE Transactions on Pattern Analysis and Machine Intelligence
Off-Line Handwritten Word Recognition Using a Hidden Markov Model Type Stochastic Network
IEEE Transactions on Pattern Analysis and Machine Intelligence
Extraction of noise robust rotation invariant texture features via multichannel filtering
ICIP '97 Proceedings of the 1997 International Conference on Image Processing (ICIP '97) 3-Volume Set-Volume 3 - Volume 3
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
Time-invariant orthonormal wavelet representations
IEEE Transactions on Signal Processing
Handwritten word recognition with character and inter-character neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Farsi Handwriting Word Recognition (FHWR), especially those with different orientation and scale changes as well as different handwriting style, is a challenging and important problem in document image analysis. This paper proposes a holistic FHWR scheme using local features of M-Band packet wavelet. The rotation and scale invariant local feature extraction for a given word image involves applying a polar transform to eliminate the rotation and scale effects, but at same time produce a row shifted polar image, which is then passed to an M-Band wavelet transform with row shift invariant to eliminate the row shift effects. So, the output M-Band wavelet coefficients are rotation and scale invariant. A local feature vector extracted from each subband of M-Band wavelet coefficients is constructed for rotation and scale invariant FHWR. In the experiments, we employed a Mahalanobis classifier to recognition a set of 224 distinct Farsi words selected from different persons with different style of writing. The experimental results, based on different handwriting style testing data sets for images with different orientations and scales, show that the proposed classification scheme using M-Band wavelet signatures outperforms other holistic handwriting word recognition methods, its overall accuracy rate for joint rotation and scale invariance being 95.8 percent, demonstrating that the extracted local energy features are effective rotation and scale invariant features.