A Simple Method for Online Recognition of Farsi Subwords
Subject Areas : electrical and computer engineering
S. M.
Razavi
1
E.
Kabir
2
(Tarbiat Modares University)
Keywords: Farsirecognitiononline handwritingsubworddotminimum distance,
Abstract :
In this paper, a method for online recognition of Farsi subwords is presented. First, the dots and other signs of the input subword and their relative locations are recognized and the related group to that subword is chosen. If there is only one member in that group, its class is assigned to the input subword, otherwise, the subword body is compared to those of the group members and the subword with minimum distance to the input subword is found. The recognition system also proposes a maximum of nine subwords in the next ranks. The proposed method was tested on a database of 11 samples of 1000 subwords from different writers. The correct recognition rate for the first choice was 74.95%. It reached to 97.87% for the top 10 choices.
[1] R. Plamondon and N. Srihari, "On-line and off-line handwriting recognition: a comprehensive survey," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 22, no. 1, pp. 63-84, Jan. 2000.
[2] S. D. Connell, Online Handwriting Recognition Using Multiple Pattern Class Models, Ph.D. Thesis, Michigan State University, 2000.
[3] C. C. Tappert, C. Y. Suen, and T. Wakahara, "The State of the art in online handwriting recognition," IEEE Trans. on Pattern Analysis and Machine Intelligence,vol. 12, no. 8, pp. 787-808, Aug. 1990.
[4] G. Seni, Large Vocabulary Recognition of On-Line Handwritten Cursive Words, Ph.D. Thesis, State University of New York, 1995.
[5] C. L. Liu, S. Jaeger, and M. Nakagawa, "Online recognition of chinese characters: the state-of-the-art," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 26, no. 2, pp. 198-213, Feb. 2004.
[6] S. Jaeger, C. L. Liu, and M. Nakagawa, "The State of the art in japanese online handwriting recognition compared to techniques in western handwriting recognition," IJDAR, vol. 6, no. 2, pp. 75-88, Oct. 2003.
[7] K. Badie and M. Shimura, "Machine recognition of arabic cursive scripts," Pattern Recognition in Practice, E. S. Gelsema and L. N. Kanal, eds., pp. 34-37, Monich, Oct. 1982.
]8[ م. شيرعلي شهرضا، تشخيص كلمات و ارقام دستنويس فارسي بوسيلة شبكه‌هاي عصبي، رسالة دكتري دانشكدة برق، دانشگاه صنعتي اميركبير، 1374.
]9[ ر. عزمي، بازشناسي متون چاپي فارسي، رسالة دكتري مهندسي برق، دانشگاه تربيت مدرس، تابستان 1378.
]10[ ك. فائز و م. شيرعلي شهرضا، "تشخيص متن چاپي فارسي با فونت ثابت با استفاده از شبه كلمات،" مجله اميركبير، سال هفتم، شمارة 28، صص 291-300، بهار و تابستان، 1374.
]11[ ك. مسروري، شناسايي برون خط كلمات دستنويس فارسي در يك مجموعة محدود، رسالة دكتري مهندسي برق، دانشگاه تربيت مدرس، تابستان 1379.
[12] A. Amin, "Recognition of printed Arabic text based on global features and decision tree learning techniques," Pattern Recognition, vol. 33, no. 8, pp. 1309-1323, Aug. 2000.
[13] R. Azmi and E. Kabir, "A New segmentation technique for omnifont farsi text", Pattern Recognition Letters, vol. 22, no. 2, pp. 97-104, Feb. 2001.
[14] M. Dehghan, K. Faez, M. Ahmadi, and M. Shridhar, "Handwritten Farsi (Arabic) word recognition: a holistic approach using discrete HMM", Pattern Recognition, vol. 34, no. 5, pp. 1057-1065, May 2001.
[15] A. Amin, A. Kaced, J. P. Haton, and R. Mohr, "Hand written Arabic character recognition by The I.R.A.C. system," in Proc. 5th Int. Conf. Pattern Recognition, pp. 729-731, Florida, Dec. 1980.
[16] A. Amin, "Machine recognition of handwritten arabic words by the IRAC II system", in Proc. 6th Int. Conf. on Pattern Recognition, pp. 34-36, Munich, Germany, 1982.
[17] S. Al-Emami and M. Usher, "On-Line recognition of handwritten arabic characters," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 12, no. 7, pp. 704-710, Jul. 1990.
[18] H. S. M. Beigi, K. Nathan,G. J. Clary, and J. Subrahmonia, "Challenges of handwriting recognition in Farsi, Arabic and other languages with similar writing styles an on-line digit recognizer," in Proc. of the 2nd Annual Conf. on Technological Advancements in Developing Countries, Columbia University, New York, Jul. 1994.
[19] A. M. Alimi, O. A. Ghorbel, "The analysis of error in an on-line recognition system of Arabic handwritten characters," in Proc 3rd Int. Conf. on Document Analysis and Recognition, vol. 2, pp. 890-893, Canada, 1995.
[20] A. M. Alimi, "A neuro-fuzzy approach to recognize Arabic handwritten characters", in Proc Int. Conf. On Neural Networks, vol. 3, pp. 1397-1400, 1997.
[21] F. Bouslama, "Structural and fuzzy techniques in the recognition of online Arabic characters," Int. Journal of Pattern Recognition and Artificial Intelligence, vol. 13, no. 7, pp. 1027-1040, Nov. 1999.
[22] N. Mezghani, M. Cheriet, and A. Mitiche, "Combination of pruned kohonen maps for on-line Arabic characters recognition," in Proc of the Seventh Int. Conf. on Document Analysis and Recognition, ICDAR 2003, pp. 900-905, 3-6 August 2003, Edinburgh, Scotland, UK 2003.
[23] ﻫ. مظفري، ف. رهگذر و ش. شريف، "تشخيص برخط ارقام دستنويس فارسي،" مجموعه مقالات دومين كنفرانس سالانه انجمن كامپيوتر ايران، صص 196-204، دانشگاه صنعتي اميركبير، تهران، 1375.
[24] ك. عباسيان، بازشناسي برخط نويسه‌هاي فارسي، پايان‌نامه كارشناسي ارشد مهندسي برق، دانشگاه تربيت مدرس، پاييز 1376.
[25] ك. عباسيان و ا. كبير، "بازشناسي برخط نويسه‌هاي فارسي،" مجموعه مقالات ششمين كنفرانس مهندسي برق ايران، جلد 3، صص 141-146، دانشگاه خواجه نصيرالدين طوسي، تهران، 1377. [26] م. رضوي و ا. كبير، "يك پايگاه داده براي بازشناسي دست‌نوشته‌هاي برخط فارسي،" ششمين كنفرانس سيستم‌هاي هوشمند، صص 218-225، كرمان، آذر 1383.
[27] م. رضوي و ا. كبير، "بازشناسي برخط حروف مجزاي فارسي،" ششمين كنفرانس سيستم‌هاي هوشمند، صص 226-234، كرمان، آذر 1383.
[28] م. رضوي و ا. كبير، "بازشناسي برخط حروف مجزاي فارسي با شبكه عصبي،" سومين كنفرانس ماشين بينايي و پردازش تصوير ايران، صص 83-89، دانشگاه تهران، اسفند 1383.
[29] م. رضوي و ا. كبير، "روشي ساده براي بازشناسي برخط حروف مجزاي فارسي،" ارسال شده به مجله دانشكده مهندسي دانشگاه فردوسي مشهد، 1383.