INFORMATION CHANGE THE WORLD

International Journal of Information Engineering and Electronic Business(IJIEEB)

ISSN: 2074-9023 (Print), ISSN: 2074-9031 (Online)

Published By: MECS Press

IJIEEB Vol.7, No.2, Mar. 2015

Printed Text Character Analysis Version-III: Optical Character Recognition with Noise Reduction, Background Detection and User Training Mechanism for Simple Cursive Fonts

Full Text (PDF, 806KB), PP.27-37


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Author(s)

Satyaki Roy, Ayan Chatterjee, Rituparna Pandit, Kaushik Goswami

Index Terms

Cursive font handling mechanism;Resizing Algorithm;Character broken lines;Noise Reduction;Background Detection

Abstract

The present system performs analysis of snapshots of cursive and non-cursive font character text images and yields customizable text files using optical character recognition technology. In the previous versions the authors have discussed the user training mechanism that introduces new non-cursive font styles and writing formats into the system and incorporates optimization, noise reduction and background detection modules. This system specifically focuses on enhancing the process of character recognition by introducing a mechanism for handling simple cursive fonts.

Cite This Paper

Satyaki Roy, Ayan Chatterjee, Rituparna Pandit, Kaushik Goswami,"Printed Text Character Analysis Version-III: Optical Character Recognition with Noise Reduction, Background Detection and User Training Mechanism for Simple Cursive Fonts", IJIEEB, vol.7, no.2, pp.27-37, 2015. DOI: 10.5815/ijieeb.2015.02.05

Reference

[1]Mori S, Suen C Y and Yamamoto K,"Historical review of OCR research and development", Proceedings of IEEE 80, pp. 1029–1058, 1992.

[2]J. Mantas,"An overview of character recognition methodologies", Pattern Recognition Volume 19, Issue 6, pp. 425–430, 1986.

[3]Mamta Maloo, K.V. Kale, "Gujurati Script Recognition: A Review", International Journal of Computer Science Issues, Vol. 8 Issue 4 No. 1, pp. 480-489, July 2011.

[4]Kwame Osei Boateng, Benjamin Weyori Asubam, David Sanka Laar, "Improving the Effectiveness of the Median Filter", International Journal of Electronics and Communication Engineering Volume 5 No. 1 (2012) pp 85-87.

[5]Ravina Mithe, Supriya Indalkar, Nilam Divekar, "Optical Character Recognition", International Journal of Recent Technology and Engineering (IJRTE) Volume 2 Issue 1, pp. 72-75, March 2013. Nick Efford, "Digital Image Processing a Practical Introduction using Java"- Pearson Education.

[6]Sukhpreet Singh, "Optical Character Recognition Techniques: A Survey" Journal of Emerging Trends in Computing and Information Sciences, Vol. 4, No. 6 June 2013.

[7]Youssef Bassil, Mohammad Alwani, "OCR Post-Processing Error Correction Algorithm Using Google's Online Spelling Suggestion", Journal of Emerging Trends in Computing and Information Sciences, Vol. 3 No.1, pp. 90-99, January 2012.

[8]Satyaki Roy, Ayan Chatterjee, Rituparna Pandit, Kaushik Goswami, "Printed Text Character Analysis Version-I: Optical Character Recognition with the new User Training Mechanism", International Journal of Advanced Computer Research, Volume 4 No. 2 Issue 15 June, 2014.

[9]Satyaki Roy, Ayan Chatterjee, Rituparna Pandit, Kaushik Goswami, "Printed Text Character Analysis Version-II: Optimized optical character recognition for noisy images with the new user training and background detection mechanism", International Journal of Advanced Computer Research Volume 4 No. 2 Issue 15 June, 2014.

[10]Herbert Schildt, "Java- The Complete Reference, 8th Edition", McGraw-Hill Companies.

[11]Kwame Osei Boateng, Benjamin Weyori Asubam, David Sanka Laar, "Improving the Effectiveness of the Median Filter", International Journal of Electronics and Communication Engineering Volume 5 No. 1 (2012) pp 85-87.

[12]Gonzalez, Woods and Eddins, "Digital Image Processing Using Matlab", Gatesmark Publishing.