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Pool of knowledge available to the mankind depends on the source of learningresources, which can vary from ancient printed documents to present electronicmaterial. The rapid conversion of material available in traditional librariesto digital form needs a significant amount of work if we are to maintain theformat and the look of the electronic documents as same as their printedcounterparts. Most of the printed documents contain not only characters and itsformatting but also some associated non text objects such as tables, charts andgraphical objects. It is challenging to detect them and to concentrate on theformat preservation of the contents while reproducing them. To address thisissue, we propose an algorithm using local thresholds for word space and lineheight to locate and extract all categories of tables from scanned documentimages. From the experiments performed on 298 documents, we conclude that ouralgorithm has an overall accuracy of about 75% in detecting tables from thescanned document images. Since the algorithm does not completely depend on rulelines, it can detect all categories of tables in a range of scanned documentswith different font types, styles and sizes to extract their formattingfeatures. Moreover, the algorithm can be applied to locate tables in multicolumn layouts with small modification in layout analysis. Treating tables withtheir existing formatting features will tremendously help the reproducing ofprinted documents for reprinting and updating purposes.

Locating Tables in Scanned Documents for Reconstructing and Republishing (ICIAfS14)