NDLI: Online Thai handwritten character recognition using hidden Markov models and support vector machines

Content Provider	IEEE Xplore Digital Library
Author	Sanguansat, P. Asdornwised, W. Jitapunkul, S.
Copyright Year	2004
Description	Author affiliation: Dept. of Electr. Eng., Chulalongkorn Univ., Bangkok, Thailand (Sanguansat, P.; Asdornwised, W.; Jitapunkul, S.)
Abstract	We propose a method for online Thai handwritten character recognition using HMMs and SVMs with score-space kernels. Score-space kernels are generalized Fisher kernels based on underlying generative models, such as Gaussian mixture models (GMMs), which are output distributions of each state in HMMs. Our system combines the advantages of both generative and discriminative classifiers. In the first phase, HMMs are used for multi-classification, then SVMs are applied to resolve any uncertainty remaining after the first-pass HMM-based recognizer, but they are not applied for all classes because the results of some classes are worse. We consider the HMM confusion matrix to find the confused candidates in each class. If there is one candidate, it means there is no confusion in this class, and HMMs alone are sufficient to classify. SVMs are applied if there is more than one candidate. If there are more than two, the multi-class method is applied. On account of the basic score-spaces, likelihood and likelihood ratio score-spaces are not symmetrical. In the case of likelihood score-space, the parameters refer to only one generative model from two class models. In the case of likelihood ratio score-space, the parameters refer to both of them, but in different positions; thus one observation sequence can map to two score-vectors. We propose a new symmetric score-space, called symmetric likelihood ratio score-space. In this way, one observation sequence is mapped to only one score-vector. Experimental results show the average recognition rate improved from 89.9%, using baseline HMM, to 92.5%, using our proposed method.
Starting Page	492
Ending Page	497
File Size	354452
Page Count	6
File Format	PDF
ISBN	0780385934
DOI	10.1109/ISCIT.2004.1412894
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-10-26
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Fuzzy logic Handwriting recognition Natural languages Hidden Markov models Support vector machine classification Speech recognition Variable speed drives Character recognition Kernel
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

On-line Thai handwritten character recognition using hidden Markov model and fuzzy logic

Online handwriting recognition with support vector machines - a kernel approach

Kernels for Large Margin Time-Series Classification

Online handwriting recognition using support vector machine

Spoken Language recognition using support vector machines with generative front-end

Handwritten character recognition using machine learning approach - a survey

Classification models for historical manuscript recognition

Modular Approach to Recognition of Strokes in Telugu Script

Hidden Markov Models for Online Handwritten Tamil Word Recognition

Online Thai handwritten character recognition using hidden Markov models and support vector machines

Similar Documents

On-line Thai handwritten character recognition using hidden Markov model and fuzzy logic

Online handwriting recognition with support vector machines - a kernel approach

Kernels for Large Margin Time-Series Classification

Online handwriting recognition using support vector machine

Spoken Language recognition using support vector machines with generative front-end

Handwritten character recognition using machine learning approach - a survey

Classification models for historical manuscript recognition

Modular Approach to Recognition of Strokes in Telugu Script

Hidden Markov Models for Online Handwritten Tamil Word Recognition

Online Thai handwritten character recognition using hidden Markov models and support vector machines