JIS  Vol.9 No.3 , July 2018
Handwritten Numeric and Alphabetic Character Recognition and Signature Verification Using Neural Network
Abstract: Handwritten signature and character recognition has become challenging research topic due to its numerous applications. In this paper, we proposed a system that has three sub-systems. The three subsystems focus on offline recognition of handwritten English alphabetic characters (uppercase and lowercase), numeric characters (0 - 9) and individual signatures respectively. The system includes several stages like image preprocessing, the post-processing, the segmentation, the detection of the required amount of the character and signature, feature extraction and finally Neural Network recognition. At first, the scanned image is filtered after conversion of the scanned image into a gray image. Then image cropping method is applied to detect the signature. Then an accurate recognition is ensured by post-processing the cropped images. MATLAB has been used to design the system. The subsystems are then tested for several samples and the results are found satisfactory at about 97% success rate. The quality of the image plays a vital role as the images of poor or mediocre quality may lead to unsuccessful recognition and verification.
Cite this paper: Nashif, M. , Miah, M. , Habib, A. , Moulik, A. , Islam, M. , Zakareya, M. , Ullah, A. , Rahman, M. and Hasan, M. (2018) Handwritten Numeric and Alphabetic Character Recognition and Signature Verification Using Neural Network. Journal of Information Security, 9, 209-224. doi: 10.4236/jis.2018.93015.

[1]   Zhang, Y., Liang, S., Nie, S., Liu, W. and Peng, S. (2018) Robust Offline Handwritten Character Recognition through Exploring Writer-Independent Features under the Guidance of Printed Data. Pattern Recognition Letters, 106, 20-26.

[2]   Patil, V. and Shimpi, S. (2011) Handwritten English Character Recognition Using Neural Network. Elixir International Journal: Computer Science and Engineering, 41, 5587-5591.

[3]   Yeung, D.-Y., et al. (2004) SVC2004: First International Signature Verification Competition. Biometric Authentication, Springer, 16-22.

[4]   Drouhard, J.-P., Sabourin, R. and Godbout, M. (1994) Evaluation of a Training Method and of Various Rejection Criteria for a Neural Network Classifier Used for Off-Line Signature Verification. IEEE International Conference on Neural Networks, IEEE World Congress on Computational Intelligence, 7, 4294-4299.

[5]   Leclerc, F. and Plamondon, R. (1994) Automatic Signature Verification: The State of the Art—1989-1993. International Journal of Pattern Recognition and Artificial Intelligence, 8, 643-660.

[6]   Ammar, M., Yoshida, Y. and Fukumura, T. (1988) Off-Line Preprocessing and Verification of Signatures. International Journal of Pattern Recognition and Artificial Intelligence, 2, 589-602.

[7]   Jain, A.K., Griess, F.D. and Connell, S.D. (2002) On-Line Signature Verification. Pattern Recognition, 35, 2963-2972.

[8]   Impedovo, D. and Pirlo, G. (2008) Automatic Signature Verification: The State of the Art. IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews), 38, 609-635.

[9]   Bose, N.K. and Liang, P. (1996) Neural Network Fundamentals with Graphs, Algorithms and Applications. McGraw-Hill Series in Electrical and Computer Engineering.

[10]   Miah, M.B.A., Yousuf, M.A., Mia, M.S. and Miya, M.P. (2015) Handwritten Courtesy Amount and Signature Recognition on Bank Cheque Using Neural Network. International Journal of Computer Applications, 118, No. 5.

[11]   Palacios, R., Gupta, A. and Wang, P.S. (2003) Feedback-Based Architecture for Reading Courtesy Amounts on Checks. Journal of Electronic Imaging, 12, 194-203.

[12]   Pal, U., Belaıd, A. and Choisy, C. (2003) Touching Numeral Segmentation Using Water Reservoir Concept. Pattern Recognition Letters, 24, 261-272.

[13]   Dewangan, S.K. (2013) Real Time Recognition of Handwritten Devnagari Signatures without Segmentation Using Artificial Neural Network. International Journal of Image, Graphics and Signal Processing, 5, 30.

[14]   Dey, S.A. (1999) Adding Feedback to Improve Segmentation and Recognition of Handwritten Numerals. PhD Thesis, Massachusetts Institute of Technology.

[15]   Guillevic, D. and Suen, C.Y. (1998) Recognition of Legal Amounts on Bank Cheques. Pattern Analysis and Applications, 1, 28-41.

[16]   Kaufmann, G. and Bunke, H. (1998) A System for the Automated Reading of Check Amounts-Some Key Ideas. International Workshop on Document Analysis Systems, Nagano, 4-6 November 1998, 188-200.

[17]   Molla, M.K.I. and Talukder, K.H. (2002) Bangla Number Extraction and Recognition from Document Image. 5th ICCIT, Dhaka, 27-28 December 2002, 200-206.

[18]   Shah, M.S., Haque, S.A., Islam, M.R., Ali, M.A. and Shabbir, M. (2010) Automatic Recognition of Handwritten Bangla Courtesy Amount on Bank Checks. International Journal of Computer Science and Network Solutions, 10, 154-163.

[19]   Lethelier, E., Leroux, M. and Gilloux, M. (1995) An Automatic Reading System for Handwritten Numeral Amounts on French Checks. Proceedings of the 3rd International Conference on Document Analysis and Recognition, Montreal, 14-16 August 1995, Vol. 1, 92-97.

[20]   Mashiyat, A.S., Mehadi, A.S. and Talukder, K.H. (2004) Bangla Off-Line Handwritten Character Recognition Using Superimposed Matrices. 7th International Conference on Computer and Information Technology, Dhaka, 26-28 December 2004, 610-614.

[21]   Brocklehurst, E.R. (1985) Computer Methods of Signature Verification. Journal of the Forensic Science Society, 25, 445-457.

[22]   Qi, Y. and Hunt, B.R. (1994) Signature Verification Using Global and Grid Features. Pattern Recognition, 27, 1621-1629.

[23]   Miah, M.B.A., Haque, S.A., Rashed Mazumder, M. and Rahman, Z. (2011) A New Approach for Recognition of Holistic Bangla Word Using Neural Network. International Journal of Data Warehousing and Mining, 1, 139-141.