Abstract This paper presents a developed geometric and logic algorithm of on-line Arabic handwriting baseline detection. It consists of two stages: the geometric first stage detects sets of nearly aligned points candidates to support the baseline by considering the accordance between the alignment of the trajectory points and their tangents directions. While the logic second stage uses topologic conditions and rules specific to the Arabic handwritten script in order to evaluate the relevance of each one of the three most extended sets of points from the extracted groups to be recognized as a baseline and then to correct the first stage detection result which is based only on the size of the group of points. The system is also designed to be able to extract the baseline of inclined and/or irregular aligned short handwritten sentence thanks to the flexibility of the used method for the constitution of sets of nearly aligned points. The iterative application of this last method in a relatively short neighborhood window sliding on a long and curved handwritten line script permits to extract its curved baseline.

References

[1] Abuzaraida M., Zeki A., and Zeki A., Segmentation Techniques for Online Arabic c/ a) Example of skewed online handwriting baseline detection b/ Detected baseline b) Example of curved online handwriting baseline detection a)Example of baseline detection of an offline handwriting reconstructed skeleton b)Projection of the detected baseline from the reconstructed skeleton trajectory to the original handwriting binary image c) Grapheme segmentation resultant of the above detected curved baseline example (b) 0100200300400500600700 0 50 100 150 450500550600650700 40 60 80 100 120 050100150200250300350 0 50 100 050100150200250300350 0 50 100 50100150200250300350400450 50 100 150 050100150200250300350 0 50 100 050100150200250300350 0 50 100 GLoBD: Geometric and Learned Logic Algorithm for Straight or Curved ... 139 Handwriting Recognition: A Survey, in Proceedings of the 3rd International Conference on Information and Communication Technology for the Moslem World, Jakarta, pp. 37-40, 2010.

[2] AL-Shatnawi A., A Novel Baseline Estimation Method for Arabic Handwritten Text Based on Exploited Components of Voronoi Diagrams, The International Arab Journal of Information Technology, vol. 13, no. 3, pp. 249-255, 2016.

[3] Alimi A., Evolutionary Computation for the Recognition of On-line Cursive Handwriting, IETE Journal of Research, vol. 48, no. 5, pp. 385-396, 2002.

[4] Boubaker H., Kherallah M., and Alimi A., New Algorithm of Straight or Curved Baseline Detection for Short Arabic Handwritten writing, in Proceedings of 10th International Conference on Document Analysis and Recognition, Barcelona, pp. 778-782, 2009.

[5] Boubaker H., Tagougui N., ElAbed H., Kherallah M., and Alimi A., Graphemes Segmentation for Arabic On-line Handwriting Modeling, Journal of Information Processing System, vol. 10, no. 4, pp. 503- 522, 2014.

[6] Chaabouni A., Boubaker H., Kherallah M., El- Abed H., and Alimi A., Static and Dynamic Features for Writer Identification Based on Multi-Fractals, The International Arab Journal of Information Technology, vol. 11, no. 4, pp. 416-424, 2014.

[7] Elanwar R., Rashwan M., and Mashali S., Simultaneous Segmentation and Recognition of Arabic Characters in an Unconstrained On-Line Cursive Handwritten Document, International Journal of Computer, Electrical, Automation, Control and Information Engineerin, vol. 1, no. 5, pp. 1326-1329, 2007.

[8] Elbaati A., Boubaker H., Kherallah M., El Abed H., Ennaji A., and Alimi A., Arabic Hand Writing Recognition Using Restored Stroke Chronology, in Proceedings of 10th International Conference on Document Analysis and Recognition, Barcelona, pp. 411-415, 2009.

[9] Eraqi H. and Abdelazeem S., An On-Line Arabic Handwriting Recognition System Based on a New On-line Graphemes Segmentation Technique, in Proceedings of 11th International Conference on Document Analysis and Recognition, Beijing, pp. 409-413, 2011.

[10] Garcia-Salicetti S., Dorizzi B., Gallinari P., and Wimmer Z., Adaptive Discrimination in an HMM-Based Neural Predictive System for On- Line Word, in Proceedings of 13th International Conference on Pattern Recognition, Vienna, pp. 515-519, 1996.

[11] Likforman-Sulem L., Hanimyan A., and Faure C., A Hough Based Algorithm for Extracting Text lines in Handwritten Documents, in Proceedings of the 3rd International Conference on Document Analysis and Recognition, Montreal, pp. 774-777, 1995.

[12] Olivier G., Miled H., Romeo K., and Lecourtier Y., Segmentation and Coding of Arabic Handwritten Words, in Proceedings of the 13th International Conference on Pattern Recognition, Vienna, pp. 264-268, 1996.

[13] Pechwitz M. and M rgner V., Baseline Estimation For Arabic Handwritten Words, In Proceedings of the 8th International Workshop on Frontiers in Handwriting Recognition, Niagara, pp. 479-484, 2002.

[14] Plamondon R. and Srihari S., On-Line and Off- Line Handwriting Recognition: A Comprehensive Survey, IEEE Transaction on Pattern Analysis and Machine Intelligence, vol. 22, no. 1, pp. 63-84, 2000.

[15] Schenk J., Lenz J., and Rigoll G., Novel Script Line Identification Method for Script Normalization and Feature Extraction in On-Line Handwritten Whiteboard Note Recognition, Pattern Recognition Journal, vol. 42, no. 12, pp. 3383-3393, 2009.

[16] Snoussi M., Bouafif S., Bouriel K., and Ellouze N., Baseline Extraction: Comparison of Six Methods on IFN/ENIT Database, in Proceedings of the International Conference on Frontiers in Handwriting Recognition, Montr al, pp. 571-576, 2008.

[17] Stahlberg F. and Vogel S., Detecting Dense Foreground Stripes in Arabic Handwriting for Accurate Baseline Positioning, in Proceedings of the 13th International Conference on Document Analysis and Recognition, Tunis, pp. 361-365, 2015.

[18] Tagougui N., Kherallah M., and Alimi A., Online Arabic Handwriting Recognition: a Survey, International Journal on Document Analysis and Recognition, vol. 16, no. 3, pp. 209 -226, 2012.

[19] Touj S., Amara N., and Amiri H., Arabic Handwritten Words Recognition Based on a Planar Hidden Markov Model, The International Arab Journal of Information Technology, vol. 2, no. 4, p 318-325, 2005

[20] Ziaratban M. and Faez K., Detection and Compensation of Undesirable Discontinuities within the Farsi/Arabic Subwords, The International Arab Journal of Information Technology, vol. 8, no. 3, pp. 293-301, 2011. 140 The International Arab Journal of Information Technology, Vol. 15, No. 1, January 2018 Houcine Boubaker graduated in Electrical Engineering in 1995, obtained a master degree in Systems Analyses and Digital Signal Processing in 1997. He is a researcher in Electrical and Computer Engineering at the University of Sfax and affiliate to the Research Groups in Intelligent Machines laboratory (ReGIM). His research interest includes trajectories modeling and pattern recognition. He focuses his research on drawing, handwriting and arm-hand movements modeling and Analyses. Aymen Chaabouni obtained the master degree in computer science, in 2006 from the University of Avignon and the Vaucluse, French and a Ph.D. from the National School of Engineering of Sfax, Tunisia in 2014. His main research interest is in the area of Handwriting recognition, Writer Identification and Signature Verification. He is member of the IEEE computer society. He is member of the Research Groups in Intelligent Machines Laboratory (ReGIM). Haikal El-Abed is a Ph.D. Senior Research Engineer at the Braunschweig Technical University, Germany. Since 2001, he has been working at the Institute for Communications Technology (IfN), Department of Signal Processing for Mobile Information Systems. He has specialized in image and signal processing, document analysis systems design and configuration, and Arabic/Latin manuscripts recognition. He coordinated different conferences and international research projects and is one of the developers of the IfN/ENIT-Database. Adel Alimi graduated in Electrical Engineering 1990, obtained a Ph.D. and then an HDR both in Electrical and Computer Engineering in 1995 and 2000 respectively. He is now professor in Electrical and Computer Engineering at the University of Sfax. His research interest includes applications of intelligent methods (neural networks, fuzzy logic, evolutionary algorithms) to pattern recognition, robotic systems, vision systems, and industrial processes He focuses his research on intelligent pattern recognition, learning, analysis and intelligent control of large scale complex systems. He is associate editor and member of the editorial board of many international scientific journals. He is an IEEE senior member.