Handwritten text recognition of Bengali is a difficult task because of complex character shapes due to the presence of modified/compound characters as well as zone-wise writing styles of different individuals. Most of the research published so far on Bengali handwriting recognition deals with either isolated character recognition or isolated word recognition, and just a few papers have researched on recognition of continuous handwritten Bengali. In this paper we present a research on continuous handwritten Bengali. We follow a classical line-based recognition approach with a system based on hidden Markov models and n-gram language models. These models are trained with automatic methods from annotated data. We research both on the maximum likelihood approach and the minimum error phone approach for training the optical models. We also research on the use of word-based language models and characterbased language models. This last approach allow us to deal with the out-of-vocabulary word problem in the test when the training set is of limited size. From the experiments we obtained encouraging results.

This work has been partially supported through the European Union’s H2020 grant READ (Recognition and Enrichment of Archival Documents) (Ref: 674943) and partially supported by MINECO/FEDER, UE under project TIN2015-70924-C2-1-R.