Hanwrittent Text Recognition for Bengali

© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.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.Sánchez Peiró, JA.; Pal, U. (2016). Hanwrittent Text Recognition for Bengali. IEEE. https://doi.org/10.1109/ICFHR.2016.010

MatriVasha: A Multipurpose Comprehensive Database for Bangla Handwritten Compound Characters

At present, recognition of the Bangla handwriting compound character has been an essential issue for many years. In recent years there have been application-based researches in machine learning, and deep learning, which is gained interest, and most notably is handwriting recognition because it has a tremendous application such as Bangla OCR. MatrriVasha, the project which can recognize Bangla, handwritten several compound characters. Currently, compound character recognition is an important topic due to its variant application, and helps to create old forms, and information digitization with reliability. But unfortunately, there is a lack of a comprehensive dataset that can categorize all types of Bangla compound characters. MatrriVasha is an attempt to align compound character, and it's challenging because each person has a unique style of writing shapes. After all, MatrriVasha has proposed a dataset that intends to recognize Bangla 120(one hundred twenty) compound characters that consist of 2552(two thousand five hundred fifty-two) isolated handwritten characters written unique writers which were collected from within Bangladesh. This dataset faced problems in terms of the district, age, and gender-based written related research because the samples were collected that includes a verity of the district, age group, and the equal number of males, and females. As of now, our proposed dataset is so far the most extensive dataset for Bangla compound characters. It is intended to frame the acknowledgment technique for handwritten Bangla compound character. In the future, this dataset will be made publicly available to help to widen the research.Comment: 19 fig, 2 tabl