Online handwritten Bangla character recognition using HMM

Parui, Susanta Kumar; Guin, Kalyan Kumar; Bhattacharya, Ujjwal; Chaudhuri, B. B.

doi:10.1109/icpr.2008.4761835

Cited by 85 publications

(39 citation statements)

References 5 publications

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“…However, there has not been much work on handwriting recognition of Indian scripts. Particularly, there have very few attempts for the recognition of online Bengali handwritten characters Parui et al, 2008). But both of these two approaches are not applicable for the development of web-based handwriting education system, because of slow recognition speed.…”

Section: Related Backgroundmentioning

confidence: 99%

An Autonomous Learning System of Bengali Characters Using Web-Based Intelligent Handwriting Recognition

Khatun¹,

Miwa²

2016

JEL

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This research project was aimed to develop an intelligent Bengali handwriting education system to improve the literacy level in Bangladesh. Due to the socio-economical limitation, all of the population does not have the chance to go to school. Here, we developed a prototype of web-based (iPhone/smartphone or computer browser) intelligent handwriting education system for autonomous learning of Bengali characters that allows students to do practice their handwriting at anywhere at any time. As an intelligent tutor, the system can automatically check the handwriting errors, such as stroke production errors, stroke sequence errors, stroke relationship errors and immediately provide colourful error feedback to the students to correct themselves. Bengali is a multi-stroke input characters with extremely long cursive shape where it has stroke order variability and stroke direction variability. Due to this structural limitation, recognition speed is a crucial issue to apply traditional online handwriting recognition algorithm. In this work, we have adopted hierarchical recognition approach to improve the recognition speed that makes our system adaptable for web-based language learning. We applied writing speed free recognition methodology together with hierarchical recognition algorithm. It ensured the learning of all aged population, especially for children and older people. Finally, we conducted a survey in Bangladesh for the performance analysis of our proposed education system. The experimental results showed that our autonomous learning methodology helped to improve the average recognition accuracy by 4.1% (from 87.2% to 91.4%) with average Mean-Opinion-Score 4.1. It confirmed that the successful use of web-based Bengali handwriting education system can be very helpful to improve the literacy level in Bangladesh within a very short period.

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Section: Related Backgroundmentioning

confidence: 99%

An Autonomous Learning System of Bengali Characters Using Web-Based Intelligent Handwriting Recognition

Khatun¹,

Miwa²

2016

JEL

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“…Overall recognition accuracy of 97.33% is reported for writer dependent system. Parui et al [19] presented a system where, Recognition of the strokes forming the shape of an unknown character sample is done by the HMM classifier. The classification rate reported by the proposed scheme is 87.7% whereas classification accuracy at the stroke level is 84.6%.…”

Section: Recognition Of Bangla Scriptmentioning

confidence: 99%

“…Practical applications of online handwriting recognition are: (i) Pen based form filling, (ii) Word processing, (iii) Natural language processing, and (iv)Usage of online handwriting recognition in conjunction with speech synthesis, to empower people with vocal disability to communicate with others [46]. Various Designers have been actively involved in developing online Handwritten character recognition systems for Indian scripts (N Joshi et al [6,27]; A Sharma et al [11,51], R.K Sharma et al [11,14,15,16], Sachan and Lehal et al [12,13] ,U. Bhattacharya et al [17,19,20], A G. Ramkrishnan etal. [6,27,30,31,32,33,34,43,44,46], R. Kunwar et al [30,31,44]).…”

Section: Introductionmentioning

confidence: 99%

Online Handwritten Text Recognition for Indian Scripts

Kaur¹,

Sharma²

2017

IOSR JVSP

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“…In [4] the authors have decomposed characters into a set of structural shape units and used s dynamic time warping based classifiers to identify component shapes in a character. Structural units called strokes have been used in [5] to identify handwritten Bengali characters using a Hidden Markov Model classifier. A histogram of chain code direction of contour points represented using a 64-dimensional feature vector have been utilized in [6] to recognized characters from 6 popular Indian scripts.…”

Section: Previous Workmentioning

confidence: 99%

Character Recognition using Dynamic Windows

Biswas¹,

Parekh²

2012

IJCA

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This paper proposes a scheme for recognition of English characters based on features derived from partitioning the character image into non-overlapping cells. A dynamic sliding window moves over each cell and pixel counts obtained from the image portion within the boundaries of the window, contribute towards generation of the feature vector. A total of four passes of the window over the image each with a different window size leads to the generation of a 30-element feature vector. A neural network (multi-layered perceptron) is used for classifying the 26 alphabets of the English language. Accuracies obtained are demonstrated to have been improved upon with respect to contemporary works.

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Online handwritten Bangla character recognition using HMM

Cited by 85 publications

References 5 publications

An Autonomous Learning System of Bengali Characters Using Web-Based Intelligent Handwriting Recognition

An Autonomous Learning System of Bengali Characters Using Web-Based Intelligent Handwriting Recognition

Online Handwritten Text Recognition for Indian Scripts

Character Recognition using Dynamic Windows

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