In the last two decades, many advances have been made in the field of document image analysis and recognition. In the recent past, several methods for recognizing Latin, Chinese, Japanese, and Arabic scripts have been proposed [7–9]. Until now, most of the OCR work has concentrated on high quality images and great success has been achieved by character recognition systems. Apart from these successes, there still exist two challenging problems in the field of recognition. The first one is optical character recognition (OCR) for low-quality images. Images having luminance variations, noise, and random degradation of text are difficult to read by OCR systems. The second open problem is that of recognizing off-line cursive handwritten character recognition [15]. Our work concentrates on the former one particularly for Devanagari script, which is the script for Hindi, Nepali, Marathi, and several other Indic languages. Together, these languages have a user base exceeding 500 million people.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bansal V, Sinha RMK (2001) A Devanagari OCR and a brief review of OCR research for Indian scripts. Proceedings of STRANS01
Chaudhari BB, Pal U (1997) An OCR system to read two Indian Languages scripts. Proc. of 4th Int. Conf. on Document Analysis and Recognition, 1011–1015
Atul Negi, Chakravarthy Bhagvati, Krishna B (2001) An OCR System for Telugu, ICDAR, 1110
Jawahar CV, Pavan Kumar MNSSK, Ravi Kiran SS (2003) A Bilingual OCR for Hindi-Telugu Documents and its Applications, ICDAR. 408–412
Xuewen Wang, Xiaoqing Ding, Changsong Liu (2002) Optimized Gabor Filters Based Feature Extraction for Character Recognition, Proc.16th International Conference on Pattern Recognition, 223–226
Qiang Huo, Yong Ge and Zhi Dan Feng, (2001) High Performance Chinese OCR Based on Gabor Features, Discriminative Feature Extraction and Model Training. Proc. IEEE International Conference on Accoustic, Speech and Signal Processing, 1517–1520
Mantas J (1986) An Overview of Character Recognition Methodologies, Pattern Recognition 19:425–430
Bozinovic RM, Srihari SN (1989) Offline Cursive Script Word Recognition. IEEE Trans on Pattern Analaysis and Machine Intelligence 11:68–83
Mori S, Suen CY, Yamamoto K (1992) Historical Review of OCR Research and Development. Proc. of IEEE 80:1029–1058
Nagy G (2000) Twenty Years of Document Image Analysis in Pattern Analysis and Machine Intelligence. IEEE Trans. on Pattern Analysis and Machine Intelligence 22:38–62
Zhang J, Yan Y, Lades M (1997) Face recognition: Eigenface, Elastic Matching, and Neural Nets. Proc. of IEEE 85:1423–1435
Juang BH and Katigiri S (1992) Discriminative Learning for Minimum Error Classification Paper Title. IEEE Trans. on Signal Processing 4:3043–3054
XuewenWang, Xiaoqing Ding and Changsong Liu (2005) Gabor Based Feature Extraction for Character Recognition. Pattern Recognition 38:369–379
Alain Biem (2006) Minimum Classification Error Training for Online Handwriting Recognition. IEEE Trans. on Pattern Analysis and Machine Intelligence 28:1041–1051
Plamondon R, Srihari SN (2000) On-line and Off-line Handwriting Recognition: A Comprehensive Survey. IEEE. Trans. on Pattern Analysis and Machine Intelligence 22:63–84
Kanungo T. et al. (2000) A Statistical, Nonparametric Methodology for Document Degradation Model Validation. IEEE Trans. on Pattern Analysis and Machine Intelligence 20:1209–1223
Chaudhuri BB and Pal U (1997) Skew Angle Detection of Digitized Indian Script Documents. IEEE Trans. on Pattern Analysis and Machine Intelligence 19:182–186
Maurer CR, Qi R, Raghavan V (2003) A Linear Time Algorithm for Computing Exact Euclidean Distance Transforms of Binary Images in Arbitrary Dimensions. IEEE Trans. on Pattern Analysis and Machine Intelligence 25:265–270
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer Science+Business Media, LLC
About this chapter
Cite this chapter
Dhingra, K.D., Sanyal, S., Sharma, P.K. (2008). A Robust OCR for Degraded Documents. In: Huang, X., Chen, YS., Ao, SI. (eds) Advances in Communication Systems and Electrical Engineering. Lecture Notes in Electrical Engineering, vol 4. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-74938-9_34
Download citation
DOI: https://doi.org/10.1007/978-0-387-74938-9_34
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-74937-2
Online ISBN: 978-0-387-74938-9
eBook Packages: EngineeringEngineering (R0)