CFP last date
22 April 2024
Reseach Article

Segmentation of Assamese Handwritten Characters based on Projection Profiles

by Sagarika Borah
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 130 - Number 17
Year of Publication: 2015
Authors: Sagarika Borah
10.5120/ijca2015907088

Sagarika Borah . Segmentation of Assamese Handwritten Characters based on Projection Profiles. International Journal of Computer Applications. 130, 17 ( November 2015), 12-17. DOI=10.5120/ijca2015907088

@article{ 10.5120/ijca2015907088,
author = { Sagarika Borah },
title = { Segmentation of Assamese Handwritten Characters based on Projection Profiles },
journal = { International Journal of Computer Applications },
issue_date = { November 2015 },
volume = { 130 },
number = { 17 },
month = { November },
year = { 2015 },
issn = { 0975-8887 },
pages = { 12-17 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume130/number17/23300-2015907088/ },
doi = { 10.5120/ijca2015907088 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:25:52.533332+05:30
%A Sagarika Borah
%T Segmentation of Assamese Handwritten Characters based on Projection Profiles
%J International Journal of Computer Applications
%@ 0975-8887
%V 130
%N 17
%P 12-17
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The most important part of a character recognition system is segmenting the characters properly and selecting the best features from the characters. This paper describes a character segmentation method for an ANN based character recognition system which is used for recognition of optically scanned handwritten Assamese character. The segmentation of characters are done using horizontal and vertical projections of the hand written text document. For feature extraction the system extracts the geometric features of the characters which are consist of basic line types that are used in the formation of the character skeleton. The feature vector of the training set generated by this system is used to train the recognition system using ANN.

References
  1. Kaustubh Bhattacharyya and Kandarpa Kumar Sarma “ANN-based Innovative Segmentation Method for Handwritten text in Assamese”, IJCSI International Journal6 of Computer Science Issues, Vol. 5, 2009,pp 9-16
  2. Mohammad Adnan Al-Alaoui, Mohammad Amin Abou Harb, Zeid Abou Chahine, and Elias Yaacoub,”A New Approach for Arabic Offline HandwritingRecognition”, IEEE multidisciplinary engineering education magazine, vol. 4, no. 3, september 2009,pp 89-97
  3. Zaidi Razak, Khansa Zulkiflee , Mohd Yamani Idna Idris, Emran Mohd Tamil, Mohd Noorzaily ,Mohamed Noor, Rosli Salleh, Mohd Yaakob ,Zulkifli Mohd Yusof and Mashkuri Yaacob,”Off-line Handwriting Text Line Segmentation : A Review” ,IJCSNS International Journal of Computer Science and Network Security, vol.8 No.7, July 2008 pp 12-20
  4. M. Arivazhagan, H. Srinivasan, S. N. Srihari.2007. A Statistical Approach to Handwritten Line Segmentation. In Proceedings of SPIE Document Recognition and Retrieval XIV , San Jose, CA, February 2007
  5. Dinesh Dileep,” A feature extraction technique based on character geometry for Character recognition”
  6. Sarma Kandarpa Kumar, Member, IEEE,”Bi-lingual Handwritten Character and Numeral Recognition using Multi-Dimensional Recurrent Neural Networks (MDRNN,)” International Journal of Electrical and Electronics Engineering 3:7 2009,pp 441-448
  7. Yusuf Perwej, Ashish Chaturvedi,” Machine Recognition of Hand Written Characters using Neural Networks”, International Journal of Computer Applications (0975 – 8887) Volume 14– No.2, January 2011,pp 6-9.
  8. Belhadef Hacene, Eutamene Aicha, Kholadi Mohamed Khireddine “Character Recognition Approach Based on Ontology”.in press. Pp 160-168
  9. Krevat Elie, Cuzzillo Elliot, “Improving Off-line Handwritten Character Recognition with Hidden Markov Models,”in press.
  10. Peyarajan S, “On-line Tamil hand written character recognition using Kohonen neural network,” Research Journal of Computer Systems Engineering- An International journal,in press.
  11. Ranpreet Kaur , Baljit Singh ,” A Hybrid Neural Approach For Character Recognition System”, International Journal of Computer Science and Information Technologies, Vol. 2 (2) , 2011, pp 721-726
  12. Fox Richard, Hartmann William, “An Abductive Approach to Hand- written Character Recognition for Multiple Domains”.
  13. Sandhya Arora, Debotosh Bhattacharjee, Mita Nasipuri, Dipak Kumar Basu, Mahantapas Kundu,” Combining Multiple Feature Extraction Techniques for Handwritten Devnagari Character Recognition ”, 2008 IEEE Region 10 Colloquium and the Third ICIIS, Kharagpur, INDIA December 8-10. Pp 1-6
  14. Sheetal Dabra, Sunil Agrawal and Rama Krishna Challa, “Novel Feature Set for Recognition Of Similar Shaped Handwritten Hindi Characters Using Machine Learning”, Cs & It 02, Pp. 25–35, 2011.
  15. Bilan Zhu and Masaki Nakagawa, “Online Handwritten Chinese/Japanese Character Recognition”, InTech,2012
  16. Sarat Saharia, Prabin K. Bora, Dilip K. Saikia,” Improving Character Recognition Accuracy of Tchebichef Moments by Splitting of Images”, NCC 2009, January 16-18, IIT Guwahati ,pp 390-393
  17. Dipak D. Bage, K. P. Adhiya, Sanjay S. Gharde ,” A New Approach For Recognizing Offline Handwritten Mathematical Symbols Using Character Geometry”, International Journal Of Innovative Research In Science, Engineering And Technology Vol. 2, Issue 7, July 2010
  18. Mamatha H R and Srikantamurthy K. Morphological Operations and Projection Profiles based Segmentation of Handwritten Kannada Document ,International Journal of Applied Information Systems (IJAIS) – ISSN : 2249-0868 Foundation of Computer Science FCS, New York, USA Volume 4– No.5,October 2012
Index Terms

Computer Science
Information Sciences

Keywords

Direction vector HCR Feature vector zone starters intersection points.