Performance Comparison of Classifiers for Bilingual Gurmukhi-Roman Online Handwriting Recognition System
Gurpreet Singh1, Manoj Kumar Sachan2

1Gurpreet Singh, Department of CSE, Chandigarh University, Punjab, India.
2Manoj Kumar Sachan, Department of CSE, Chandigarh University, Punjab, India.

Manuscript received on 18 June 2019 | Revised Manuscript received on 25 June 2019 | Manuscript published on 30 June 2019 | PP: 573-581 | Volume-8 Issue-5, June 2019 | Retrieval Number: E7151068519/19©BEIESP
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (

Abstract: Bilingual or multilingual script recognitionsystems for Online handwriting recognition (OHR) have been considered as a hot area of research. Here, more than one scripts have been used to generate handwriting samples.Unique writing style of each script increases the complexity of these systems. In this paper, performance comparison of classifiers is presented for bilingual Gurmukhi-Roman OHR system. This proposed systemprocessed intermixed bilingual handwritten text inputted through a digitizer tablet and pen. Various steps like input of handwriting samples, pre-processing, segmentation, feature extraction, classification and post-processing have been implemented to get digital data. The main emphasis has been given to the classification phase. Three different classifiers Multi-Layered Perceptron (MLP) neural network, Support Vector Machine (SVM) and Hidden Markov Model (HMM) have been implemented. The performance of these classifiers hasbeen compared and it is observed that MLPshows better results as compare to SVM and HMM classifiers.
Keywords: Online Handwriting Recognition, Bilingual, Multilingual, Gurmukhi, Roman, MLP, SVM, HMM.

Scope of the Article: Pattern Recognition