TY  - CONF
AU  - Volkmar Frinken
AU  - Alicia Fornes
AU  - Josep Llados
AU  - Jean-Marc Ogier
A2  - SSPR&SPR
PY  - 2012//
TI  - Bidirectional Language Model for Handwriting Recognition
T2  - LNCS
BT  - Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshop
SP  - 611
EP  - 619
VL  - 7626
PB  - Springer Berlin Heidelberg
N2  - In order to improve the results of automatically recognized handwritten text, information about the language is commonly included in the recognition process. A common approach is to represent a text line as a sequence. It is processed in one direction and the language information via n-grams is directly included in the decoding. This approach, however, only uses context on one side to estimate a word’s probability. Therefore, we propose a bidirectional recognition in this paper, using distinct forward and a backward language models. By combining decoding hypotheses from both directions, we achieve a significant increase in recognition accuracy for the off-line writer independent handwriting recognition task. Both language models are of the same type and can be estimated on the same corpus. Hence, the increase in recognition accuracy comes without any additional need for training data or language modeling complexity.
SN  - 0302-9743
SN  - 978-3-642-34165-6
L1  - http://refbase.cvc.uab.es/files/FFL2012.pdf
UR  - http://dx.doi.org/10.1007/978-3-642-34166-3_67
N1  - DAG
ID  - Volkmar Frinken2012
ER  -