Fast Lexicon-Based Word Recognition in Noisy Index Card Images

This paper describes a complete system for reading typewritten 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.

• Type: Conference
• Year: 2003
• Where: ICDAR
• Authors: Simon M. Lucas, Gregory Patoulas, Andy C. Downton
• Keywords: Document Analysis, ICDAR 2003, Museum Index Cards, Typewritten Lexicon Words, first Stage Extracts