Share This Article:

Handwritten Numeric and Alphabetic Character Recognition and Signature Verification Using Neural Network

Full-Text HTML XML Download Download as PDF (Size:1210KB) PP. 209-224
DOI: 10.4236/jis.2018.93015    506 Downloads   1,151 Views

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.

Copyright © 2019 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.