HMM-Based Online Handwriting Recognition System for Telugu Symbols

Babu, V. Jagadeesh; Prasanth, L.; Sharma, Rajiv Kumar; Rao, G. Appa; Bharath, Anil A.

doi:10.1109/icdar.2007.4378676

Cited by 42 publications

(26 citation statements)

References 3 publications

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“…HMM-based Online Handwriting Recognition System for Telugu Symbols is developed by Babu et al [38]. They introduced a cost-effective and natural data collection procedure based on ACECAD Digimemo.…”

Section: Recognition Of Telugu Scriptmentioning

confidence: 99%

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Online Handwritten Text Recognition for Indian Scripts

Kaur¹,

Sharma²

2017

IOSR JVSP

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Section: Recognition Of Telugu Scriptmentioning

confidence: 99%

“…[6,27,30,31,32,33,34,43,44,46], R. Kunwar et al [30,31,44]). Little work has also been reported for bilingual Online HCR(S Lakshami et al [7,35], A.Arora and Namboodiri et al [38]) and HCR for Mobile Devices (A Sharma et al [52]). In this paper an overview of online handwritten character recognition for Indian scripts is reported.…”

Section: Introductionmentioning

confidence: 99%

Online Handwritten Text Recognition for Indian Scripts

Kaur¹,

Sharma²

2017

IOSR JVSP

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“…Well known probabilistic model Bayesian decision rule is applied for OHCR in Babu et al (2007). A probabilistic discriminate model Conditional Random Fields is applied in Kerrick and Bovik (1988).…”

Section: Statistical Modelsmentioning

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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“…The pre-processing stage consists of size normalization, smoothing, interpolation of missing points, removal of duplicate points and resampling of the captured coordinates [10].…”

Section: Pre-processing and Feature Extractionmentioning

confidence: 99%

“…In our system, we examine the effectiveness of using Hidden Markov Models (HMM) and Support Vector Machines (SVM) for modelling the classifier. HMM has been used for Bangla [2], Telugu [3], Tamil [4], Malayalam [5] and in previous works for Assamese [6] [7]. Support vector machines (SVMs) have also been used in [8] for Telegu and Devnagiri scripts while [9] compares the performance between systems developed using HMM and SVM for Telegu script.…”

Section: Introductionmentioning

confidence: 99%