Abstract Artificial Neural Networks (ANN) were used in the recognition of the printed Arabic text with a high rate of success. In contrast, Arabic hand-writing recognition has many challenges, some were tackled in some research recently. In this paper we used ANN in recognizing Arabic hand-written characters with the Genetics Algorithm (GA). The GA was used to search for the best ANN structure. We consider Arabic off-line characters represented by a series of (x, y) coordinate. The dataset was gathered from a couple of volunteers, used the E-pen to write different Arabic letters. A Matrix Laboratory (Mat Lab) program was implemented to store the written characters and extracts their features. Features were determined based on the shape and number of segments that made up the characters. The recognition results were very promising when using ANN with the GA in comparison with other relevant approaches. On average more than 95% of accuracy was achieved when GA is used to adjust ANN structure in order to get the best recognition rate.

References

[1] Abuhaiba I., Mahmoud S., and Green R., Recognition of Handwritten Cursive Arabic Characters, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 16, no. 6, pp. 664-672, 1994.

[2] Al-Emami S. and Usher M., On-Line Recognition of Handwritten Arabic Characters, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12, no. 7, pp. 704-710. 1990.

[3] Al-Hajj M., Likforman-Sulem L., and Mokbel C., Combining Slanted-Frame Classifiers for Improved HMM-Based Arabic Handwriting Recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 7, pp. 1165-1177, 2009.

[4] Alma adeed S., Recognition of Off-Line Handwritten Arabic Words Using Neural Network, in Preccedings of Geometric Modeling and Imaging--New Trends, England, pp. 141-44, 2006.

[5] Ben-Amara N., Mazhoud O., Bouzrara N., and Ellouze N., ARABASE: A Relational Database for Arabic OCR Systems, The International Arab Journal Of Information Technology, vol. 2, no. 4, pp. 259-266, 2005.

[6] Chan K. and Yeung D., Recognizing on-Line Handwritten Alphanumeric Characters through Flexible Structural Matching, Pattern Recognition, vol. 32, no. 7, pp. 1099-1114, 1999.

[7] Omer M. and Ma S., 2010. Online Arabic Handwriting Character Recognition Using Matching Algorithm, in Preccedings of 2nd International Conference on Computer and Automation Engineering, Singapore, pp. 259-62, 2010.

[8] Zahour A., Taconet B., Mercy P., and Ramdane S., Arabic Hand-Written Text-Line Extraction, in Proceedings of 6th International Conference on Document Analysis and Recognition, Seattle, pp. 281-285, 2001.

[9] Zimmermann M., Chappelier J., and Horst B., Offline Grammar-Based Recognition of Handwritten Sentences, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 5, pp. 818-821, 2006. Khalid Nahar is an assistant professor in the Department of Computer Science, Yarmouk University, Irbid-Jordan. He received his BS and MS degrees in computer science from Yarmouk University in Jordan, in 1992 and 2005 respectively. He was awarded a full scholarship to continue his PhD in Computer Science and Engineering from King Fahd University of Petroleum and Minerals (KFUPM), KSA. In 2013 he completed his PhD and started his job as an assistant proof. at Tabuk University, KSA. He served at Tabuk University from 2013 to 2015. For now he is working at Yarmouk University-Jordan, as an assistant professor in the Department of Computer Science. His research interests include: continuous speech recognition, Arabic computing, natural language processing, multimedia computing, content-based retrieval, artificial intelligence, and software engineering. He had published several papers in his field and gain a fund for his project titled Knowledge and Intelligent Speech Recognition System To Assist Handicapped Persons under the number S-1436-0012 From Tabuk University, KSA.