ANUPAM GARG

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Vrije Universiteit Brussel

Jaydip Sen

Praxis Business School

Bhaskar Nuthanakanti

Jawaharlal Nehru Technological University

Annappa B

National Institute of Technology Karnataka,Surathkal

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RIVERS STATE POLYTECHNIC

Dr. Vinod Kumar

Kurukshetra University

Benny Boikanyo Latlhang

Management College of Southern Africa (MANCOSA)

Jana Javornik

University of East London

Gwen Robbins Schug

University of North Carolina at Greensboro

Gabriel Gutierrez-Alonso

University of Salamanca

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Offline handwritten Gurmukhi character recognition: k-NN vs. SVM classifier

International journal of information technology, Nov 15, 2019

In this paper, we analyzed the impact of combination of feature extraction and classification tec... more In this paper, we analyzed the impact of combination of feature extraction and classification techniques for recognition of offline handwritten segmented Gurmukhi characters. Also principal component analysis (PCA) has been used to find efficient features from peak extent based and modified division point (MDP) based features which have further been used in the classification process. For classification, k-NN and SVM whose three different kernels, namely, linear-SVM, polynomial-SVM and RBF-SVM have been considered for recognition accuracy in this paper. The experimentation has been performed on the dataset of 8960 samples of offline handwritten Gurmukhi characters written by 160 unique writers. For selecting the training and testing dataset, five different partitioning strategies and k-fold cross validation techniques have been used. A recognition accuracy of 92.3% has been achieved, using the combination of linear-SVM, polynomial-SVM and k-NN classifiers and with the partitioning strategy of 80% data as training dataset and remaining data as testing dataset.

Degraded offline handwritten Gurmukhi character recognition: study of various features and classifiers

International journal of information technology, Nov 29, 2019

Recognition of degraded offline handwritten characters of Gurmukhi script is very challenging tas... more Recognition of degraded offline handwritten characters of Gurmukhi script is very challenging task due to the complex structural properties of the script, which is not matter-of-fact in majority of other scripts. A study based on the combination of various feature extraction techniques for character recognition has been presented in this paper. By extracting statistical features in hierarchical order from the pre-segmented degraded offline handwritten Gurmukhi characters, the potential results are analyzed for the recognition. Four types of feature extraction techniques, namely, zoning, diagonal, peak extent based features (horizontally and vertically) and shadow features have been considered in the present study. For classification, three classifiers, specifically, k-NN, decision tree and random forest are employed to demonstrate the effect on the problem of degraded offline handwritten Gurmukhi character recognition. Authors have collected 8960 samples which are partitioned using the partitioning strategy and fivefold cross validation technique. In partitioning strategy, 80% of data is taken as the training dataset and remaining 20% data is considered as the testing dataset. Various parameters for performance measures such as recognition accuracy, false rejection rate (FRR), area under curve (AUC) and root mean square error (RMSE) are also used for analyzing the performance of features and classifiers.

ANUPAM GARG

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