58 research outputs found
Handwritten Character Recognition of South Indian Scripts: A Review
Handwritten character recognition is always a frontier area of research in
the field of pattern recognition and image processing and there is a large
demand for OCR on hand written documents. Even though, sufficient studies have
performed in foreign scripts like Chinese, Japanese and Arabic characters, only
a very few work can be traced for handwritten character recognition of Indian
scripts especially for the South Indian scripts. This paper provides an
overview of offline handwritten character recognition in South Indian Scripts,
namely Malayalam, Tamil, Kannada and Telungu.Comment: Paper presented on the "National Conference on Indian Language
Computing", Kochi, February 19-20, 2011. 6 pages, 5 figure
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
State Of The Art In Digital Paleography
Digital paleography is an approach used to assist paleographers in deciding the origin of manuscripts. This is done by recording types of writings present in old manuscripts. It uses digital representation of book hands as a tool to support paleographical analyses by, human experts. There are six types of manuscripts selected which are Arabic, Chinese, Jawi, Indian, Latin and Roman. These types of manuscripts are discussed through their current contribution in the digital paleography field. The main purpose of this paper is to discuss the current work on digital paleography for selected types of manuscripts. Thus, we identified the approaches and methods used to define the types of handwritings in old manuscript
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