• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology


Indian Journal of Science and Technology

Year: 2022, Volume: 15, Issue: 16, Pages: 712-717

Original Article

Classification of North and South Indian Handwritten Scripts using Gabor Wavelet Features

Received Date:12 January 2022, Accepted Date:28 February 2022, Published Date:29 April 2022


Objectives: Handwritten script identification plays a vital role in processing handwritten data electronically. Most of the methods fail to provide accuracy due to variation in handwriting, hence the classification of the Indic script before providing it to OCR is crucial. The anticipated work helps increase the accuracy by categorizing the handwritten documents as north or South Indic script before further classification. Methods: This study has proposed a method, using Gabor filters to extract features from the text image for recognizing the kind of script, and seven widely used Indian scripts were considered for this experiment. The handwritten documents were collected from distinct individuals on request, under supervision. The database was manually created by extracting portions of lines from the scanned document images. Findings: A recognition accuracy of 100% was obtained for classifying North and South scripts while an average accuracy of 92% was obtained for biscript classification using KNN classifier at a portion of the line level. Novelty: The proposed method improves the accuracy by acting as a pre-processor to the OCR system by classifying the script according to North Indian script or South Indian Script. Further, it can be processed to find out the script type within the North or South Indian Scripts.

Keywords: Handwritten Script; Gabor Filter; KNN Classifier; OCR; Indic Script


  1. Rajput DGG, H.B. A. Handwritten Script Recognition using DCT, Gabor Filter and Wavelet Features at Line Level. International Journal of Electronics Signals and Systems. 2011;1(2):85–90. doi: 10.47893/IJESS.2011.1017
  2. Bhunia AK, Mukherjee S, Sain A, Bhunia AK, Roy PP, Pal U. Indic handwritten script identification using offline-online multi-modal deep network. Information Fusion. 2020;57:1–14. Available from: https://dx.doi.org/10.1016/j.inffus.2019.10.010
  3. Obaidullah SM, Santosh KC, Das N, Halder C, Roy K. Handwritten Indic Script Identification in Multi-Script Document Images: A Survey. International Journal of Pattern Recognition and Artificial Intelligence. 2018;32(10):1856012. Available from: https://dx.doi.org/10.1142/s0218001418560128
  4. Balaha HM, Ali HA, Saraya M, Badawy M. A new Arabic handwritten character recognition deep learning system (AHCR-DLS) Neural Computing and Applications. 2021;33(11):6325–6367. Available from: https://dx.doi.org/10.1007/s00521-020-05397-2
  5. Sharma A, Jayagopi DB. Towards efficient unconstrained handwriting recognition using Dilated Temporal Convolution Network. Expert Systems with Applications. 2021;164:114004. Available from: https://dx.doi.org/10.1016/j.eswa.2020.114004
  6. Altwaijry N, Al-Turaiki I. Arabic handwriting recognition system using convolutional neural network. Neural Computing and Applications. 2021;33(7):2249–2261. Available from: https://dx.doi.org/10.1007/s00521-020-05070-8
  7. Inunganbi S, Choudhary P, Manglem K. Meitei Mayek handwritten dataset: compilation, segmentation, and character recognition. The Visual Computer. 2021;37(2):291–305. Available from: https://dx.doi.org/10.1007/s00371-020-01799-4
  8. Khamparia A, Singh SK, Luhach AK. SVM-PCA Based Handwritten Devanagari Digit Character Recognition. Recent Advances in Computer Science and Communications. 2021;14(1):48–53. Available from: https://dx.doi.org/10.2174/2213275912666181219092905
  9. Rajput GG, Ummapure SB. Script Identification from Handwritten document Images Using LBP Technique at Block level. 2019 International Conference on Data Science and Communication (IconDSC). 2019;p. 1–6. doi: 10.1109/IconDSC.2019.8816944
  10. Fernandes R, Rodrigues AP. Kannada Handwritten Script Recognition using Machine Learning Techniques. 2019 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER). 2019;p. 1–6. doi: 10.1109/DISCOVER47552.2019.9008097
  11. Shirke A, Gaonkar N, Pandit P, Parab K. Handwritten Gujarati Script Recognition. 2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS). 2021;p. 1174–1179. doi: 10.1109/ICACCS51430.2021.9441811
  12. Singh PK, Sarkar R, Abraham A, Nasipuri M. A Case Study on Handwritten Indic Script Classification: Benchmarking of the Results at Page, Block, Text-line, and Word Levels. ACM Trans. Asian Low-Resour. Lang. Inf. Process. 2021;21(2):36. Available from: https://doi.org/10.1145/3476102
  13. Guha R, Ghosh M, Singh PK, Sarkar R, Nasipuri M. A Hybrid Swarm and Gravitation-based feature selection algorithm for handwritten Indic script classification problem. Complex & Intelligent Systems. 2021;7(2):823–839. Available from: https://dx.doi.org/10.1007/s40747-020-00237-1


© 2022 Shreesha & Anita. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Published By Indian Society for Education and Environment (iSee)


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