Title: Script invariant handwritten digit recognition using a simple feature descriptor
Authors: Pawan Kumar Singh; Supratim Das; Ram Sarkar; Mita Nasipuri
Addresses: Department of Computer Science and Engineering, Jadavpur University, 188 Raja S.C. Mullick Road, Kolkata-700032, West Bengal, India ' Department of Computer Science and Engineering, Jadavpur University, 188 Raja S.C. Mullick Road, Kolkata-700032, West Bengal, India ' Department of Computer Science and Engineering, Jadavpur University, 188 Raja S.C. Mullick Road, Kolkata-700032, West Bengal, India ' Department of Computer Science and Engineering, Jadavpur University, 188 Raja S.C. Mullick Road, Kolkata-700032, West Bengal, India
Abstract: Handwritten digit recognition is still considered as a difficult task because of the large variability of the digits shapes written by individuals. A lot of work have been done towards digit identification with excellent performance but mostly these works have been made focusing on digits written in a particular script. Hence, in a multilingual country like India, where different scripts are prevalent, methods which recognise numerals written in a single script may not always serve the purpose. To address this issue, we propose a script invariant handwritten digit recognition scheme in this paper. A novel feature extraction technique named as quadrangular transition count has been introduced. Experimentations performed using five conventional classifiers advocate that multi layer perceptron (MLP) is best among them which yields recognition accuracies of 98.33%, 97.85%, 96.72% and 95.35% on four popularly used scripts of the world namely, Arabic, Bangla, Devanagari, and Roman respectively.
Keywords: handwritten digit recognition; quadrangular transition count features; Arabic script; Bangla script; Devanagari script; Roman script; ADBase; HDRC 2013.
DOI: 10.1504/IJCVR.2018.095005
International Journal of Computational Vision and Robotics, 2018 Vol.8 No.5, pp.543 - 560
Received: 20 Nov 2016
Accepted: 22 Jan 2018
Published online: 28 Sep 2018 *