NDLI: A Machine Learning Approach to Detection of Core Region of Online Handwritten Bangla Word Samples

Content Provider	IEEE Xplore Digital Library
Author	Baral, S. Bhattacharya, S. Chakraborty, A. Bhattacharya, U. Parui, S.K.
Copyright Year	2014
Description	Author affiliation: Comput. Vision & Pattern Recognition Unit, Indian Stat. Inst., Kolkata, India (Baral, S.; Bhattacharya, S.; Chakraborty, A.; Bhattacharya, U.; Parui, S.K.)
Abstract	Core region detection of handwritten cursive words is an important step towards their automatic recognition. Several preprocessing operations such as height normalization, slant estimation etc. Are often based on this core region. This is particularly useful for word recognition of major Indian scripts, which have large character sets. The main parts of majority of these characters belong to the core region that is bounded above by a headline and bounded below by an imaginary base line. Only a few such characters or their parts appear either above or below the core region. A few approaches are available in the literature for detection of such a core region of offline handwritten word samples of Latin script. Also, a similar region is often determined for recognition of images of printed Indian scripts. However, none of these approaches have studied detection of core region of an unconstrained online handwritten word. In this article, we propose a novel method for detection of the core region of online handwritten word samples of Bangla, a major Indian script. For this we first perform smoothing on the samples and then segment a stroke into sub strokes. We compute certain novel positional features from each such sub stroke. Using these features, a multilayer perceptron (MLP) is trained by back propagation (BP) algorithm. On the basis of the output of the MLP, we determine the position of both the headline and the baseline. We have tested this approach on a recently developed large database of online unconstrained handwriting Bangla word samples. The proposed approach would also work on similar samples of Devanagari, another major Indian script. Experimental results are encouraging.
Starting Page	458
Ending Page	463
File Size	309010
Page Count	6
File Format	PDF
ISBN	9781479943357
ISSN	21676445
e-ISBN	9781479943340
DOI	10.1109/ICFHR.2014.83
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-09-01
Publisher Place	Greece
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Vectors Training Feature extraction Shape Databases Compounds Histograms Machine learning based appoach Online handwriting recognition Detection of core region of handwritten words
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Direction Code Based Features for Recognition of Online Handwritten Characters of Bangla

Lexicon Reduction Technique for Bangla Handwritten Word Recognition

A Machine Learning Approach for Classifying Offline Handwritten Arabic Words

Combination of Features for Efficient Recognition of Offline Handwritten Devanagari Words

HMM Based Online Handwritten Bangla Character Recognition Using Dirichlet Distributions

Bangla handwritten character recognition using deep belief network

A Comparison of Feature and Pixel-Based Methods for Recognizing Handwritten Bangla Digits

Offline isolated bangla handwritten character recognition using spatial relationships

A HMM- based approach for online Uyghur word handwriting recognition

A Machine Learning Approach to Detection of Core Region of Online Handwritten Bangla Word Samples

Similar Documents

Direction Code Based Features for Recognition of Online Handwritten Characters of Bangla

Lexicon Reduction Technique for Bangla Handwritten Word Recognition

A Machine Learning Approach for Classifying Offline Handwritten Arabic Words

Combination of Features for Efficient Recognition of Offline Handwritten Devanagari Words

HMM Based Online Handwritten Bangla Character Recognition Using Dirichlet Distributions

Bangla handwritten character recognition using deep belief network

A Comparison of Feature and Pixel-Based Methods for Recognizing Handwritten Bangla Digits

Offline isolated bangla handwritten character recognition using spatial relationships

A HMM- based approach for online Uyghur word handwriting recognition

A Machine Learning Approach to Detection of Core Region of Online Handwritten Bangla Word Samples