A Scale Space Approach for Automatically Segmenting Words from Historical Handwritten Documents
IEEE Transactions on Pattern Analysis and Machine Intelligence
Boosted decision trees for word recognition in handwritten document retrieval
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Self-training Strategies for Handwriting Word Recognition
ICDM '09 Proceedings of the 9th Industrial Conference on Advances in Data Mining. Applications and Theoretical Aspects
Self-training for handwritten text line recognition
CIARP'10 Proceedings of the 15th Iberoamerican congress conference on Progress in pattern recognition, image analysis, computer vision, and applications
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Handwriting Recognition (HR) has been successfully used in several applications such as postal address interpretation [1], bank check reading [2], and forms reading [3]. These applications are all characterized by small or fixed lexicons afforded by contextual knowledge. Machine recognition of handwriting in historical documents presents two primary challenges: (i) large lexicons (over 10,000 words) leading to low recognition accuracy (less than 50%) and (ii)a need for high speed HR given the millions of handwritten manuscripts in Digital Library repositories and that the speed is usually inversely proportional to lexicon size. This paper addresses the issue of speed when dealing with large lexicons. We present several techniques to improve the processing speed for a gain of up to 7 times in matching time and describe a method whereby the large lexicon is divided into smaller sets and processed in parallel. With 4 processors 18 times speedup for the matching phase is achieved.