Abstract
In a country like India, many of the documents such as office letters, checks, envelopes, forms, and other types of manuscripts are multiscript in nature. A document consisting of English script and a regional script is quite common. Hence, automatic recognition of scripts present in a multiscript document has a variety of practical and commercial applications in banks, post offices, reservation counters, libraries, etc. In this paper, a multiple feature-based approach is presented to identify the script type from a multiscript document. Features are extracted using Gabor filters, discrete cosine Transform, and wavelets of Daubechies family. Nine popular Indian scripts are considered for recognition in this paper. Experiments are performed to test the recognition accuracy of the proposed system at word level for bilingual scripts. Using neural network classifier, the average success rate is found to be 97 %.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Elgammmal AM, Ismail MA (2001) Techniques for language identification for hybrid arabic-english document images. In: Proceedings of sixth international conference on document analysis and recognition, pp 1100–1104
Pal U, Sinha S, Chaudhuri BB (2003) Multi-script line identification from indian documents, ICDAR, vol 2, pp 880, Seventh International Conference on Document Analysis and Recognition, vol 2
Pal U, Chaudhuri BB (1999) script line separation from indian multi-script documents, 5th ICDAR, pp 406–409
Pal U, Chaudhury BB (2002) Identification of different script lines from multi-script documents. Image Vis Comput 20(13–14):945–954
Pal U, Chaudhuri BB (2001) Automatic identification of English, Chinese, Arabic, Devanagari and Bangla script line. In: Proceedings of 6th international conference on document analysis and recognition (ICDAR’OI), pp 790–794
Padma MC, Vijaya PA (2010) Script identification from trilingual documents using profile based features. Int J Comput Sci Appl, Technomath Res Foundation 7(4):16–33
Abirami S, Manjula D (2009) A survey of script identification techniques for multi-script document images. Int J Recent Trends Eng 1(2)
Rajput GG, Anita HB (2010) Handwritten script recognition using DCT and wavelet features at block level, IJCA, Special issue on RTIPPR (3):158–163
Rajput GG, Anita HB (2010) Kannada, English, and Hindi handwritten script recognition using multiple features. In: Proceedings of national seminar on recent trends in image processing and pattern recognition, ISBN: 93-80043-74-0, pp 149–152
Rajput GG, Anita HB (2009) A two step approach for deskewing handwritten and machine printed document images using histograms and geometric features. In: Proceedings of Second International Conference on Signal and Image Processing, pp 414–417
Gonzalez RC, Woods RE (2008) Digital image processing, 3edn. Pearson Education, New Jersey
Yegnanarayana B (2004) Artificial neural network, PHI Publications, India
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer India
About this paper
Cite this paper
Rajput, G.G., Anita, H.B. (2013). Handwritten Script Recognition Using DCT, Gabor Filter, and Wavelet Features at Word Level. In: Chakravarthi, V., Shirur, Y., Prasad, R. (eds) Proceedings of International Conference on VLSI, Communication, Advanced Devices, Signals & Systems and Networking (VCASAN-2013). Lecture Notes in Electrical Engineering, vol 258. Springer, India. https://doi.org/10.1007/978-81-322-1524-0_44
Download citation
DOI: https://doi.org/10.1007/978-81-322-1524-0_44
Published:
Publisher Name: Springer, India
Print ISBN: 978-81-322-1523-3
Online ISBN: 978-81-322-1524-0
eBook Packages: EngineeringEngineering (R0)