TY - GEN
T1 - Fast Handwriting Recognition for Indexing Historical Documents
AU - Govindaraju, Venu
AU - Xue, Hanhong
PY - 2004
Y1 - 2004
N2 - 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 Machine recognition of handwriting in historical documents presents two primary challenges: (i) large lexicons (over 10,000 words) leading to low recognition accuracy (less than 50%) and (ii) a need for high speed HR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.
AB - 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 Machine recognition of handwriting in historical documents presents two primary challenges: (i) large lexicons (over 10,000 words) leading to low recognition accuracy (less than 50%) and (ii) a need for high speed HR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.
UR - https://www.scopus.com/pages/publications/1942516350
U2 - 10.1109/DIAL.2004.1263260
DO - 10.1109/DIAL.2004.1263260
M3 - Conference contribution
AN - SCOPUS:1942516350
SN - 076952088X
SN - 9780769520889
T3 - Proceedings - First International Workshop on Document Image Analysis for Libraries - DIAL 2004
SP - 314
EP - 320
BT - Proceedings First International Workshop on Document Image Analysis for Libraries - DIAL 2004
T2 - Proceedings First International Workshop on Document Image Analysis for Libraries DIAL 2004
Y2 - 23 January 2004 through 24 January 2004
ER -