Symbol Spotting in Digital Libraries

Symbol Spotting in Digital Libraries
Author :
Publisher : Springer Science & Business Media
Total Pages : 183
Release :
ISBN-10 : 9781849962087
ISBN-13 : 1849962081
Rating : 4/5 (081 Downloads)

Book Synopsis Symbol Spotting in Digital Libraries by : Marçal Rusiñol

Download or read book Symbol Spotting in Digital Libraries written by Marçal Rusiñol and published by Springer Science & Business Media. This book was released on 2010-05-25 with total page 183 pages. Available in PDF, EPUB and Kindle. Book excerpt: Pattern recognition basically deals with the recognition of patterns, shapes, objects, things in images. Document image analysis was one of the very ?rst applications of pattern recognition and even of computing. But until the 1980s, research in this ?eld was mainly dealing with text-based documents, including OCR (Optical Character Recognition) and page layout analysis. Only a few people were looking at more speci?c documents such as music sheet, bank cheques or forms. The community of graphics recognition became visible in the late 1980s. Their speci?c interest was to recognize high-level objects represented by line drawings and graphics. The speci?c pattern recognition problems they had to deal with was raster-to-graphics conversion (i.e., recognizing graphical primitives in a cluttered pixel image), text-graphics separation, and symbol recognition. The speci?c problem of symbol recognition in graphical documents has received a lot of attention. The symbols to be recognized can be musical notation, electrical symbols, architectural objects, pictograms in maps, etc. At ?rst glance, the symbol recognition problems seems to be very similar to that of character recognition; - ter all, characters are basically a subset of symbols. Therefore, the large know-how in OCR has been extensively used in graphical symbol recognition: starting with segmenting the document to extract the symbols, extracting features from the s- bols, and then recognizing them through classi?cation or matching, with respect to a training/learning set.


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