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Segmentation of Handwritten Text in Gurmukhi Script
Rajiv K. Sharma, Amardeep Singh
Pages - 12 - 17     |    Revised - 06-08-2008     |    Published - 16-09-2008
Volume - 2   Issue - 3    |    Publication Date - June 2008  Table of Contents
Character Segmentation, Middle Zone, Upper Zone, Lower Zone, Touching Characters
Character segmentation is an important preprocessing step for text recognition. The size and shape of characters generally play an important role in the process of segmentation. But for any optical character recognition (OCR) system, the presence of touching characters in textual as well handwritten documents further decreases correct segmentation as well as recognition rate drastically. Because one can not control the size and shape of characters in handwritten documents so the segmentation process for the handwritten document is too difficult. We tried to segment handwritten text by proposing some algorithms, which were implemented and have shown encouraging results. Algorithms have been proposed to segment the touching characters. These algorithms have shown a reasonable improvement in segmenting the touching handwritten characters in Gurmukhi script.
CITED BY (21)  
1 Arora, S., Sharma, D., & Arora, S. Recognition of Gurmukhi Text from Sign Board Images Captured from Mobile Camera.
2 Naveena, C., Aradhya, V. M., & Niranjan, S. K. (2013). eCS: Enhanced Character Segmentation–A Structural Approach for Handwritten Kannada Scripts. In Mining Intelligence and Knowledge Exploration (pp. 289-298). Springer International Publishing.
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5 Rajiv, K. (2012). Detection and segmentation of touching handwritten Gurmukhi script.
6 Chitrakala, S., Mandipati, S., Preethi Raj, S., & Asisha, G. (2012). An efficient character segmentation based on VNP algorithm. Research Journal of Applied Sciences, Engineering and Technology, 4(24), 5438-5442.
7 Malakar, S., Ghosh, P., Sarkar, R., Das, N., Basu, S., & Nasipuri, M. (2011, December). An improved offline handwritten character segmentation algorithm for Bangla script. In IICAI (pp. 71-90).
8 Kumar, R., & Singh, A. (2011). Hybrid Algorithm, to segment Character in Gurmukhi Handwritten Text, with a Comparative Study. Int. J. on Recent Trends in Engineering & Technology, 5(01).
9 Sarkar, R., Malakar, S., Das, N., Basu, S., Kundu, M., & Nasipuri, M. (2011). Word extraction and character segmentation from text lines of unconstrained handwritten Bangla document images. Journal of Intelligent Systems, 20(3), 227-260.
10 Singh, H. Detection of Bold and Italic Character in Gurmukhi Script. IOSR Journal of Computer Engineering (IOSRJCE) ISSN, 2278-0661.
11 Kumar, R., & Singh, A. (2011). Algorithm to Detect and Segment Gurmukhi Handwritten Text into Lines, Words and Characters. IACSIT International Journal of Engineering and Technology, 3(4), 392-395.
12 Kumar, R., & Singh, A. (2011). Character Segmentation in Gurumukhi Handwritten Text using Hybrid Approach. International Journal of Computer Theory and Engineering, 3(4), 499-501.
13 Sachan, M. K., Lehal, G. S., & Jain, V. K. (2011). A Novel Method to Segment Online Gurmukhi Script. In Information Systems for Indian Languages (pp. 1-8). Springer Berlin Heidelberg.
14 Mohanty, S., & Bebartta, H. N. D. (2010). A Novel Approach for Bilingual (English-Oriya) Script Identification and Recognition in a Printed Document. International Journal of Image Processing (IJIP), 4(2), 175.
15 Rajiv, K. S., & Amardeep, S. D. (2010). Challenges in segmentation of text in handwritten Gurmukhi script. Information Processing and Management, 388-392.
16 Sarkar, R., Malakar, S., Das, N., Basu, S., & Nasipuri, M. (2010). A Script Independent Technique for Extraction of Characters from Handwritten Word Images. International Journal of Computer Applications, 1(23), 85-90.
17 Kaur, A., Sharma, R. K., & Singh, A. (2010). A Hybrid Approach to Classify Gurmukhi Script Characters. International Journal of Recent Trends in Engineering and Technology, 3(2), 103-105.
18 Kumar, R., & Singh, A. (2010). Detection and Segmentation of Handwritten Text in Gurmukhi Script using Flexible Windowing. International Journal of Computer Theory and Engineering, 2(3), 329-332.
19 Kumar, R., & Singh, A. (2010, February). Detection and segmentation of lines and words in Gurmukhi handwritten text. In Advance Computing Conference (IACC), 2010 IEEE 2nd International (pp. 353-356). IEEE.
20 DIA, I. (2009). hybrid approach to classify gurmukhi script characters (Doctoral dissertation, Thapar University Patiala).
21 Angadi, S. A., & Kodabagi, M. M. (2009). A texture based methodology for text region extraction from low resolution natural scene images. International Journal of Image Processing, 3(5), 229-245.
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Mr. Rajiv K. Sharma
Thapar University - India
Dr. Amardeep Singh
Punjabi University - India