Statistical Language Models for On-line Handwritten Sentence Recognition
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Rejection strategies for offline handwritten text line recognition
Pattern Recognition Letters
Rejection strategies for offline handwritten text line recognition
Pattern Recognition Letters
Offline arabic handwritten text recognition: A Survey
ACM Computing Surveys (CSUR)
Generation of learning samples for historical handwriting recognition using image degradation
Proceedings of the 2nd International Workshop on Historical Document Imaging and Processing
Separability versus prototypicality in handwritten word-image retrieval
Pattern Recognition
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This paper investigates the impact of bigram and trigram language models on the performance of a Hidden Markov Model (HMM) based offline recognition system for handwritten sentences. The language models are trained on the LOB corpus which is supplemented by various additional sources of text, including sentences from additional corpora and random sentences produced by a stochastic context-free grammar (SCFG). Experimental results are provided in terms of test set perplexity and performance of the corresponding recognition systems. For the text recognition experiments handwritten material from the IAM database has been used.