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. https://doi.org/10.1016/j.patrec.2018.02.006

has been cited by the following article:

• TITLE: Handwritten Numeric and Alphabetic Character Recognition and Signature Verification Using Neural Network

  AUTHORS: Md. Hasan Hasnain Nashif, Md. Badrul Alam Miah, Ahsan Habib, Autish Chandra Moulik, Md. Shariful Islam, Mohammad Zakareya, Arafat Ullah, Md. Atiqur Rahman, Md. Al Hasan

  KEYWORDS: Signature, Handwritten Character, Image Processing, Feature Extraction, Neural Network Recognition

  JOURNAL NAME: Journal of Information Security, Vol.9 No.3, July 26, 2018

  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.