Survey and bibliography of Arabic optical text recognition
Signal Processing
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Omnifont Open-Vocabulary OCR System for English and Arabic
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognition of Off-Line Handwritten Arabic Words Using Hidden Markov Model Approach
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Printed arabic character recognition using HMM
Journal of Computer Science and Technology
Offline Arabic Handwriting Recognition: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using the generalized Radon transform for detection of curves in noisy images
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 04
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This paper describes a technique for the recognition of off-line handwritten Arabic (Indian) numerals using Radon-Fourier-based features. A two stage classification scheme is used. The Nearest Mean (NMC), K-Nearest Neighbor (K-NNC), and Hidden Markov Models (HMMC) Classifiers are used in the first stage and a Structural Classifier (SC) is used in the second stage. A database of 44 writers with 48 samples per digit each totaling 21120 samples are used for training and testing of this technique. A number of experiments are conducted to estimate the suitable number of projections and number of Radon-Fourier-based features using the NMC and K-NNC. In addition, several experiments are conducted for estimating the suitable number of states and observations for the HMM. These experimentally estimated parameters are used in further analysis of the different classifiers. The average overall recognition rate, after the second stage, is 98.66%, 98.33%, 97.1% using NMC, K-NNC, HMMC, respectively. The presented technique proved its effectiveness in the off-line Arabic (Indian) handwritten digit recognition.