PT Unknown
AU Volkmar Frinken
   Alicia Fornes
   Josep Llados
   Jean-Marc Ogier
TI Bidirectional Language Model for Handwriting Recognition
BT Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshop
PY 2012
BP 611
EP 619
VL 7626
DI 10.1007/978-3-642-34166-3_67
AB 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.
ER