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Abstract
Handwriting Recognition System has been studied in the last few decades. Many approaches are presented to recognize the hand written documents or paper. These approaches focus on how we recognize our hand written words and lining documents. Today there is no. of application area of handwriting recognition system. So an overview of hand writing recognition system and their evolution is presented by available technique with their superiorities and limitations are reviewed. So current status of handwriting recognition is focusing on off-line and online handwriting recognition system. This overview represent as an update for the state of art in the hand writing recognition field.
Keywords – Handwriting recognition, offline and online handwriting recognition, segmentation, feature extraction and training.##plugins.themes.academic_pro.article.details##
How to Cite
Yadav, P., & Popli, M. N. (2014). Handwriting Recognition System – A Survey. International Journal of Emerging Trends in Science and Technology, 1(03). Retrieved from http://igmpublication.org/ijetst.in/index.php/ijetst/article/view/122
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16) Bazzi, R. Schwartz, J. Makhoul, “An Omnifont Open Vocabulary OCR System for English and Arabicâ€, IEEE Trans. Pattern Analysis and Machine Intelligence,vol.21, no.6, pp.495-504, 1999.
2) Dempster, A.P., N.M. Laird and D.B. Rubin. 1977. “Maximum Likelihood from Incomplete Data via the EM Algorithm.†Journal of Royal Statistical Society B 39(1):1–38.
3) Fink, G. A., M. Wienecke and G. Sagerer. 2001. Video-based on-line handwriting recognition. In Proc. 6th ICDAR. pp. 226–230.
4) Forney, G. D. 1973. The Viterbi algorithm. In Proc. IEEE. Vol. 61 pp. 268–278.
5) G¨unter, S. and H. Bunke. 2004. “HMM-based handwritten word recognition: on the optimization of the number of states, training Iterations and Gaussian components.†Pattern Recognition 37:2069–2079.
6) Johansson, S. 1986. The tagged LOB Corpus: User’s Manual. Norwegian Computing Centre for the Humanities, Norway.
7) Marti, U.-V. and H. Bunke. 2001. “Using a statistical language model to improve the performance of an HMM-based cursive handwriting recognition system.†IJPRAI 15:65 – 90.
8) Marti, U.-V. and H. Bunke. 2002. “The IAM-database: an English sentence database for offline handwriting Recognition.†IJDAR 5:39 – 46.
9) Moore, D. 2002. The IDIAP Smart Meeting Room. Technical report IDIAP-Com.
10) BLUMENSTEIN, M., and VERMA, B. (1999): A new segmentation algorithm for handwritten word recognition. Proc.
11) IJCNN International Joint Conference on Neural Networks, Washington DC, USA, 872–882.
12) BUNKE, H., ROTH, M., and SCHUKAT-TALAMAZZINI, E.G. (1995): Offline cursive handwriting recognition using Hidden Markov models. Proc. Pattern Recognition, 28(9): 1399–1413.
13) EASTWOOD, B., JENNINGS, A., and HARVEY, A. (1997): A feature based neural network segmenter for handwritten
14) Words. Proc. ICCIMA International Conference on Computational Intelligence and Multimedia Applications, Gold Coast, Australia, 286–290.
15) C. Y. Suen, C. C. Tappert, T.Wakahara, “The State of the Art in On-Line Handwriting Recognitionâ€, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 12, no. 8, pp.787-808, 1990.
16) Bazzi, R. Schwartz, J. Makhoul, “An Omnifont Open Vocabulary OCR System for English and Arabicâ€, IEEE Trans. Pattern Analysis and Machine Intelligence,vol.21, no.6, pp.495-504, 1999.