Comparison of HMM and SDTW for Tamil handwritten character recognition

Shashikiran, K; Kunwar, Rituraj; Prasad, K. N. S. S. V.; Ramakrishnan, A. G.

doi:10.1109/spcom.2010.5560498

Cited by 17 publications

(6 citation statements)

References 5 publications

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“…Efforts have been made to build offline, online and also combined systems [8]. These works include the Bangla basic character recognizer using Hidden Markov Model (HMM) and sub-stroke features [4], an online handwritten character recognition system for Telugu symbols using HMM [5], an online Tamil character recognition system using HMM and spatial dynamic time warping (SDTW) [6], an online handwritten character recognizer developed for Telugu and Malayalam [7], handwriting recognition system for Kannada numerals using K-nearest neighbour classifier [11] and Arabic handwriting recognition system using projection profiles [12]. Further, notes written on a white board are recognized by combining offline and online systems [8].…”

Section: Introductionmentioning

confidence: 99%

Combined online and offline assamese handwritten numeral recognizer

Reddy

Sharma

Prasanna

et al. 2012

2012 National Conference on Communications (NCC)

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This work describes the development of an Assamese handwritten numeral recognizer. Online handwritten numeral recognition system is developed using x, y coordinates as the feature and Hidden Markov Model (HMM) as the modelling technique. Offline handwritten numeral recognition system is developed using vertical projection profile and horizontal projection profile (VPP-HPP), zonal discrete cosine transform (DCT), chain code histogram (CCH) and pixel level information as features and vector quantization (VQ) as the modelling technique. The confusion patterns of online and offline systems are analysed. Based on this, the two systems are further combined to obtain a final numeral recognition system. The combined system exhibits improved performance over the individual approaches, demonstrating the significance of different natures of information present in each mode.

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Section: Introductionmentioning

confidence: 99%

Combined online and offline assamese handwritten numeral recognizer

Reddy

Sharma

Prasanna

et al. 2012

2012 National Conference on Communications (NCC)

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“…The most common local features are x-y coordinates, local direction features, i.e. the relative vector of two adjacent points (Shashikiran et al, 2010). Global feature is defined as a relative vector between any arbitrary points (Rampalli and Ramakrishnan, 2011).…”

Section: Local Vs Global Featuresmentioning

confidence: 99%

Preprocessing of online handwritten Telugu character recognition

Inuganti¹,

Ramisetty

2017

Int. j. adv. appl. sci.

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Online Handwritten Character Recognition (OHCR) is the method of recognizing characters by a machine while the user writes, in which the handheld devices record (x, y) coordinates of the track of the character. With the advent of handheld devices, there is a great attention towards OHCR of regional languages. Preprocessing is the main phase, in OHCR, as it increases the performance of succeeding phases, by removing the inconsistency or the redundancy present in the data collected in real-world environment. In this paper, we depict the model of Preprocessing of Online Handwritten Telugu Strokes. The preprocessing steps we address in our article are Normalization, Smoothing, Duplicate Point Removal, Interpolation, Dehooking and Resampling. Preprocessing data performance is evaluated through parameters namely recognition accuracy, recognition speed, false acceptance rate and false rejection rate over HP labs dataset hpl-Telugu-ISO-char-online-1.0. The dataset contains samples of the 166 character classes collected of different writers on ACECAD Digimemo (A4 sized) using an AcecadDigi memo DCT application. It consists of 270 samples on average for each of 166 Telugu "characters" written by native Telugu writers.

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“…For pre-processing, we normalized each character, maintaining their aspect ratio and also resampled each character into 60 equidistant points. The five features extracted per point are a) x-coordinate b) y-coordinate c) x-derivative d) y-derivative [6] and e) pen-direction angle as described in [8].…”

Section: Experimental Evaluationmentioning

confidence: 99%

Semi-supervised Online Bayesian Network Learner for Handwritten Characters Recognition

Kunwar

Pal

Blumenstein

2014

2014 22nd International Conference on Pattern Recognition

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This work addresses the problem of creating a Bayesian Network based online semi-supervised handwritten character recognisor, which learns continuously over time to make a adaptable recognisor. The proposed method makes learning possible from a continuous inflow of a potentially unlimited amount of data without the requirement for storage. It highlights the use of unlabelled data for boosting the accuracy, especially when labelled data is scarce and expensive unlike unlabelled data. An algorithm is introduced to perform semi-supervised learning based on the combination of novel online ensemble of the Randomized Bayesian network classifiers and a novel online variant of the Expectation Maximization (EM) algorithm. We make use of a novel varying weighting factor to modulate the contribution of unlabelled data. Proposed method was evaluated using online handwritten Tamil characters from the IWFHR 2006 competition dataset.The accuracy obtained was comparable to the state of the art batch learning methods like HMM and SVMs.

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Comparison of HMM and SDTW for Tamil handwritten character recognition

Cited by 17 publications

References 5 publications

Combined online and offline assamese handwritten numeral recognizer

Combined online and offline assamese handwritten numeral recognizer

Preprocessing of online handwritten Telugu character recognition

Semi-supervised Online Bayesian Network Learner for Handwritten Characters Recognition

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