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This paper presents a revised method for keyword search from handwritten digital ink in comparison with the previous system. We adopt a search method using noise reduction. Experiments on digital ink databases show that the revised method typically improves the systempsilas overall accuracy (f-measure) from 0.653 to
Facing high error rates and slow recognition speed for full text transcription of unconstrained handwriting images, keyword spotting is a promising alternative to locate specific search terms within scanned document images. We have previously proposed a learning-based method for keyword spotting using character hidden
displayed when a particular concept is happening or has happened. This paper presents a concept identification framework, based on matched keywords from overlaid-text extraction and recognition. Possible occurrences of overlaid-text in soccer programs are extracted and recognized, and then matched against a soccer-term
This paper presents a revised method for keyword search from Japanese handwritten digital ink. We employ Japanese string recognition and produce a candidate lattice. We search for a given keyword into the lattice so that we can search for the keyword even if constituent characters are not in the top candidates. We
techniques use word bounding box ratio feature initially for matching words in the database of compressed document images. For all the matching test-words, the word spotting strategy in the first model is to decompress and OCR first two characters, and then match with the keyword characters. If the matching is successful, then
by supervised learning. Experiments on TUAT Kuchibue database show that the proposed method can effectively improve the system performance. When the search method with the optimal threshold retrieves for a keyword consisting of two, three or four characters, its f-measure is 0.720, 0.868 or 0.923, respectively.
of new courses, there is a need of new model answers to the wide range of questions being asked. To address aforementioned issues effectually, this paper presents an approach which allows users to interact with paper documents, books and answer sheets etc. and to evaluate performance based on keywords harmonized with a
-grams and correction rules. The second step uses query terms, error-grams, and correction rules to create searchable keywords, identify appropriate matching terms, and determine the degree of relevance of retrieved document images. Algorithms has been tested on 979 document images provided by Media-team databases from
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