NDLI: A comparison of HMM, Naïve Bayesian, and Markov model in exploiting knowledge content in digital ink: A case study on handwritten music notation recognition

Content Provider	IEEE Xplore Digital Library
Author	Kian Chin Lee Phon-Amnuaisuk, S. Choo Yee Ting
Copyright Year	2010
Description	Author affiliation: Faculty of Information Technology, Multimedia University, Cyberjaya, Malaysia (Kian Chin Lee; Phon-Amnuaisuk, S.; Choo Yee Ting)
Abstract	The performance of a model is dependent not only on the amount of knowledge available to the model but also on how the knowledge is exploited. We investigate the recognition of handwritten musical notation based on three related probabilistic inference techniques: Hidden Markov Models (HMMs), Markov Models (MMs) and Naïve Bayes (NBs). Music notes are written on a tablet. A sequence of ink patterns representing this symbol is captured and subsequently employed for constructing the models of HMMs, MMs and NBs. The proposed approach exploits both global and local information derived from ink patterns which we have demonstrated the exploitation of this information via different features employed in different HMMs. The specificity and sensitivity measures of these classification models are compared using unseen test datasets. The findings show that HMM outperformed MM and NB models, due to the ability of HMM in exploiting both transitional probability (transition matrix A) and the overall likelihood of the observed events (emission matrix B). Also, HMMs with more hidden states outperformed those with less states, since a larger model has more capacity. In conclusion, our approach demonstrated that HMM can better exploit information extracted from ink patterns than models of MM or NB, and therefore is an optimal inference technique to encoding useful information for musical notation representation.
Starting Page	292
Ending Page	297
File Size	1150474
Page Count	6
File Format	PDF
ISBN	9781424474912
ISSN	1945788X
e-ISBN	9781424474936
DOI	10.1109/ICME.2010.5583292
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-07-19
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hidden Markov models Ink Niobium Markov processes Handwriting recognition Numerical models Music Representing and Reasoning about Music Pen-based Music Editing Recognizing Handwritten Music Notation Hidden Markov Model
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

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