This paper describes a complete system for reading type-written lexicon words in noisy images - in this case museum index cards. The system is conceptually simple, and straightforward to implement. It involves three stages of processing. The first stage extracts row-regions from the image, where each row is a hypothesized line of text. The next stage scans an OCR classifier over each row image, creating a character hypothesis graph in the process. This graph is then searched using a priority-queue based algorithm for the best matches with a set of words (lexicon). Performance evaluation on a set of museum archive cards indicates competitive accuracy and also reasonable throughput. The priority queue algorithm is over two hundred times faster than using flat dynamic programming on these graphs.
Fast lexicon-based word recognition in noisy index card images
2003-01-01
278550 byte
Conference paper
Electronic Resource
English
Fast Lexicon-Based Word Recognition in Noisy Index Card Images
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