HMM Based Approach for Handwritten Arabic Word Recognition Using the IFN/ENIT- Database
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Pre-processing Methods for Handwritten Arabic Documents
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Offline Arabic Handwriting Recognition: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Histogram clustering and hybrid classifier for handwritten Arabic characters recognition
SPPRA'06 Proceedings of the 24th IASTED international conference on Signal processing, pattern recognition, and applications
Baseline Image Classification Approach Using Local Minima Selection
IVIC '09 Proceedings of the 1st International Visual Informatics Conference on Visual Informatics: Bridging Research and Practice
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SACH'06 Proceedings of the 2006 conference on Arabic and Chinese handwriting recognition
New dynamic classifiers selection approach for handwritten recognition
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
Offline arabic handwritten text recognition: A Survey
ACM Computing Surveys (CSUR)
Determining points on handwritten mathematical symbols
CICM'13 Proceedings of the 2013 international conference on Intelligent Computer Mathematics
International Journal of Knowledge-based and Intelligent Engineering Systems
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Baseline information has been used for diverse purposes in handwriting research. The baseline represents a first orientation in a word and it is often a precondition for subsequentalgorithms, including preprocessing tasks, segmentation and feature extraction for recognition systems. Approaches based on the horizontal projection histogram are used for Arabic printed text but they are ill-suited for Arabic handwritten words. In this paper we present a method thatis completely based on polygonally approximated skeleton processing. The central algorithm is concerned with finding features in the skeleton and processing linear regression analysis. Our method performs very well as long as the model assumption of one straight line applies. We tested the method on 26459 isolated Tunisian town names written by 411 writers (IFN/ENIT-database).