Off-Line Cursive Script Word Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
On-line recognition of handprinted characters: survey and beta tests
Pattern Recognition
Character recognition—a review
Pattern Recognition
One-Pass Parallel Thinning: Analysis, Properties, and Quantitative Evaluation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Inductive Logic Programming: Techniques and Applications
Inductive Logic Programming: Techniques and Applications
Learning Logical Definitions from Relations
Machine Learning
Segmentation and Pre-Recognition of Arabic Handwriting
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
Lexicon reduction using dots for off-line Farsi/Arabic handwritten word recognition
Pattern Recognition Letters
Recognition of off-line printed Arabic text using Hidden Markov Models
Signal Processing
Accurate tool based on JPEG image compression for Arabic handwritten character shape recognition
AIA '08 Proceedings of the 26th IASTED International Conference on Artificial Intelligence and Applications
Offline arabic handwritten text recognition: A Survey
ACM Computing Surveys (CSUR)
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Character recognition systems can contribute tremendously to the advancement of the automation process and can improve the interaction between man and machine in many applications, including office automation, cheque verification and a large variety of banking, business and data entry applications. The main theme of this paper is the automatic recognition of hand-printed Arabic characters using machine learning. Conventional methods have relied on hand-constructed dictionaries which are tedious to construct and difficult to make tolerant to variation in writing styles. The advantages of machine learning are that it can generalize over the large degree of variation between writing styles and recognition rules can be constructed by example.The system was tested on a sample of handwritten characters from several individuals whose writing ranged from acceptable to poor in quality and the average correct recognitions rate obtained using cross-validation was 86.65%.