This survey aims to present Handwritten digit recognition technique. The handwritten digit recognition technique is extremely nonlinear problem. Recognition of handwritten numerals plays an active role in day to day life now days. Office automation, e-governors and many other areas are reading printed or handwritten documents and convert them to digital media is very crucial and time consuming task. So the system should be designed in such a way that it should be capable of reading handwritten numerals and provide appropriate response as humans do. However, handwritten digits are varying from person to person because each one has their own style of writing, means the same digit or character/word written by a different writer will be different even in different languages. This paper presents a survey on handwritten digit recognition systems with recent techniques, with three well known classifiers namely MLP, SVM and can used for classification. This paper presents a comparative analysis that describes recent methods and helps to find future scope.

This paper presents a comparative analysis that describes recent methods and helps to find future scope.

[1] Vineet Singh, sunil Pranit Lal(2014). Digit Recognition Using Single Layer Neural Network with Principal Component Analysis(online),available: https://ieeexplore.ieee.org/abstract/document/7053842/
[2] S.Espana-boquera, M.J.Castro-Bleda, J. Gorge-Moya(2011) Improving offline handwritten text recognition with Hybrid HMM/ANN models.
[3] D.Claudia Ciresan and Veli Meier, L.Maria and J. Schmidhuber (2011) Convolution Neural Network Committes for handwritten Character Classification.
[4] O.awadele and O.Jegeda(2009). Neural Network and Its Application In Engineering.(IJRE)Inaugural Issue,ISSN 1043-7134.
[5] Lorigo, L. M. &Venu, G., (2006). “Off-line Arabic handwritten recognition” IEEE, vol . 28, Pages:40,1-40.
[6] Al-Rashaideh H. (2006).Preprocessing phase for Arabic word handwritten recognition. Том 6, № 1, 2006, стр. 11-19
[7] Teredesai, A. Ratzlaff, E. Subrahmonia J. &Govindaraju V. (2002). On-Line digit recognition using off-line features”.vol.6.No.1,pp.11-19 ICVGIP 2002, Proceedings of the Third Indian Conference on Computer Vision, Graphics Image Processing, Ahmadabad, India. (Online), available: http://www.ee.iitb.ac.in/~icvgip/PAPERS/321.pdf.
[8] Klassen, T. (2001). Towards neural network recognition of handwritten Arabic letters.(1989)vol19,no.1,pp.1-12 (Online), available: http://users.cs.dal.ca/~mheywood/Reports/TKlassen.pdf
[9] Mashor, M. Y., Sulaiman, S. N., (2001). Recognition of Noisy Numerals using Neural Network. International Journal of the Computer, the Internet and Management, vol. 9(3), 45-52
[10] Al-Imo˘glu, F. &Alpaydin, E., (2001). Combining multiple representations for penbased handwritten digit recognition. Turk J Elec Engin, vol.9, no.1, Istanbul-Turkey.
[11] Jain, A. K. &Zongker D. (1997). Representation and recognition of handwritten digits using deformable templates. IEEE transactions on pattern analysis and machine intelligence, vol. 19, no. 12 pp. 1386-1390.
[12] S. N. Srihari, E. Cohen, J. J. Hull and L. Kuan, A system to locate and recognize ZIP codes in hand- written addresses, IJRE 1, 37-45 (1989).
[13] L. Lam and C. Y. Suen, Structural classification and relaxation matching of totally unconstrained hand- written Zip-code numbers, Pattern Recognition 21, 19- 31 (1988).
[14] M. Shridhar and A. Badreldin, Recognition of isolated and simply connected handwritten numerals, Pattern Recognition 19, 1-12 (1986).
[15] M. Shridhar and A. Badreldin, A high-accuracy syn- tactic recognition algorithm for handwritten numerals,IEEE Trans. Syst. Man Cybern. 15, 152-158 (1985).