Abstract
The presence of non-text components in the document image hinders the result of an optical character recognition (OCR)-based document analysis system. Thus, text and non-text separation has become an essential task in the domain of document image processing. To address this issue, in the present work, a simple two-stage method is developed to separate the text and the non-text components from the images of handwritten scientific documents. Before starting the actual process, connected components from the document pages are extracted. Then, in the first stage, some commonly occurred components are identified and separated out as graphics. In the second stage, remaining components are passed through feature extraction and subsequent classification processes. Evaluating the system on handwritten scientific document images, it is found that 87.16% components are classified correctly as text or non-text.
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Bhowmik, S., Kundu, S., De, B.K., Sarkar, R., Nasipuri, M. (2019). A Two-Stage Approach for Text and Non-text Separation from Handwritten Scientific Document Images. In: Chandra, P., Giri, D., Li, F., Kar, S., Jana, D. (eds) Information Technology and Applied Mathematics. Advances in Intelligent Systems and Computing, vol 699. Springer, Singapore. https://doi.org/10.1007/978-981-10-7590-2_3
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DOI: https://doi.org/10.1007/978-981-10-7590-2_3
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