Model based table cell detection and content extraction from degraded document images
Proceeding of the workshop on Document Analysis and Recognition
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This paper presents a novel set of image enhancement algorithms for binary images of poorly scanned real world page documents. Problems that are targeted by the methods described include large blobs or clutter noise, salt-and-pepper noise and detection and removal of non-text objects such as form lines or rule-lines. The algorithms described are shown to be very effective in removing clutter noise and pepper noise as well as form lines and rule-lines. A region growing algorithm is also described to enhance the quality of the text and to fix the problems arising from the salt noise which leaves holes in the text and creates broken strokes. The methods were tested on 204 images from the challenge set of the DARPA MADCAT Arabic handwritten document image data. The results indicate that the methods described are robust and are capable of significantly improving the image quality for downstream OCR systems.