A Novel GA Based OCR Enhancement and Segmentation Methodology for Marathi Language in Bimodal Framework
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Automated learning systems used to extract information from images play a major role in document analysis. Optical character recognition or OCR has been widely used to automatically segment and index the documents from a wide space. Most of the methods used for OCR recognition and extraction like HMM’s, Neural etc, mentioned in literature have errors which require human operators to be rectified and fail to extract images with blur as well as illumination variance. This paper explains proposes an enhancement supported threshold based pre-processing methodology for word spotting in Marathi printed bimodal images using image segmentation. The methodology makes use of an enhanced image obtained by histogram equalization followed by followed by age segmentation using a specific threshold. The threshold can be obtained using genetic algorithms. GA based segmentation technique is codified as an optimization problem used efficiently to search maxima and minima from the histogram of the image to obtain the threshold for segmentation. The system described is capable of extracting normal as well as blurred images and images for different lighting conditions. The same inputs are tested for a standard GA based methodology and the results are compared with the proposed method. The paper further elaborates the limitations of the method.
KeywordsOCR Genetic Algorithm Bimodal Blur Illumination
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- 3.Di Gesh, V.: A Clustering Approach to Texture Classification. In: Jain, A.K. (ed.) Real Time Object and Environment Measurement and Classification. NATO AS1 Series F, vol. 42, Springer, Heidelberg (1988)Google Scholar
- 7.IP, H.H.-S., Chan, S.-L.: Hypertext-Assisted Video Indexing and Content- based Retrieval. ACM 0-89791-866-5, 232–233 (1997)Google Scholar
- 8.Storn, R., Price, K.: Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces. Journal of Global Optimization, 341–359 (1997)Google Scholar