Title :
A comparison of character n-grams and dictionaries used for script recognition
Author :
Brakensiek, Anja ; Rigoll, Gerhard
Author_Institution :
Dept. of Comput. Eng., Gerhard-Mercator-Univ., Duisburg, Germany
fDate :
6/23/1905 12:00:00 AM
Abstract :
In this paper an off-line script recognition system is described, which makes use of a language model, that consists of backoff character n-grams. The performance of this open vocabulary recognition is compared with the use of closed dictionaries. The system is based on Hidden Markov Models (HMMs) using a hybrid modeling technique, which depends on a neural vector quantizer The presented recognition results refer to the SEDAL-database of degraded English documents such as photocopy, or fax and a writer-dependent handwritten database of cursive German script samples. Our resulting system for character recognition yields significantly, better recognition results for an unlimited vocabulary using language models
Keywords :
feature extraction; handwritten character recognition; hidden Markov models; SEDAL-database; backoff character n-grams; cursive German script samples; degraded English documents; dictionaries; fax; hidden Markov models; hybrid modeling technique; language models; neural vector quantizer; off-line script recognition system; open vocabulary recognition; photocopy; writer dependent handwritten database; Character recognition; Context modeling; Degradation; Dictionaries; Handwriting recognition; Hidden Markov models; Neural networks; Robustness; Spatial databases; Vocabulary;
Conference_Titel :
Document Analysis and Recognition, 2001. Proceedings. Sixth International Conference on
Conference_Location :
Seattle, WA
Print_ISBN :
0-7695-1263-1
DOI :
10.1109/ICDAR.2001.953791