A System for Offline Recognition of Handwritten Characters in Malayalam Script

John, J.; Balakrishnan, Kalpana; Pramod, K Varshney

doi:10.5815/ijigsp.2013.04.07

Cited by 14 publications

(4 citation statements)

References 16 publications

(14 reference statements)

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“…The gradient features have been successfully applied for Malayalam character recognition [1][2][3][4][5] . Here, the preprocessed images are divided into 2 x 2 zones and then gradient directions are computed for each of the zones using 3 x 3 sobel operators.…”

Section: Gradient Featuresmentioning

confidence: 99%

Multiple Classifier System for Offline Malayalam Character Recognition

Chacko¹,

Dhanya²

2015

Procedia Computer Science

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This paper presents a multiple classifier system for the recognition of offline handwritten Malayalam characters. The features used are the gradient and density based features. These feature sets are fed as input to two feedforward neural networks. The results of both these neural networks are combined using four different combination schemes: Max rule, Sum rule, Product rule and Borda count method. The best combination ensemble with an accuracy of 81.82% is obtained by using the Product rule combination scheme.

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Section: Gradient Featuresmentioning

confidence: 99%

Multiple Classifier System for Offline Malayalam Character Recognition

Chacko¹,

Dhanya²

2015

Procedia Computer Science

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“…Fundamental character structure components called radicals which correspond to character strokes or sub-strokes are used to model character for character recognition in the studies done by Ma et al [6], Ma et al [5] and Zhang et al [18]. Stroke is represented as a string of shape features by Shankar et al [11] to identify an unknown stroke. Study done by Bercu et al [2] classifies successive sub-strokes in a character as loops, humps and cusps and uses these as features to build HMM based recognition system.…”

Section: Introductionmentioning

confidence: 99%

Diabetic Foot: “The Next Public Health Crisis for India”

Sharma¹,

Belehalli²

2023

J Indian Inst Sci

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Direction properties of online strokes are used to analyze them in terms of homogeneous regions or sub-strokes with points satisfying common geometric properties. Such sub-strokes are called sub-units. These properties are used to extract sub-units from Hindi ideal online characters. These properties along with some heuristics are used to extract sub-units from Hindi online handwritten characters.A method is developed to extract point stroke, clockwise curve stroke, counter-clockwise curve stroke and loop stroke segments as sub-units from Hindi online handwritten characters. These extracted sub-units are close in structure to the sub-units of the corresponding Hindi online ideal characters.Importance of local representation of online handwritten characters in terms of sub-units is assessed by training a classifier with sub-unit level local and character level global features extracted from characters for character recognition. The classifier has the recognition accuracy of 93.5% on the testing set. This accuracy is the highest when compared with that of the classifiers trained only with global features extracted from characters in the same training set and evaluated on the same testing set.Sub-unit extraction algorithm and the sub-unit based character classifier are tested on Hindi online handwritten character dataset. This dataset consists of samples from 96 different characters. There are 12832 and 2821 samples in the training and testing sets, respectively.

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“…One of the clear examples is a postal company, where the task of sorting a large volume of letters and parcels is an acute issue. Many researchers have made different types of handwritten text recognition systems for different languages such as English [2,3,4], Chinese [5], Arabic [9], Japanese [6], Bangla [7], Malyalam [8], etc. Having said that, the recognition problems of these scripts cannot be considered be entirely solved.…”

Section: Introductionmentioning

confidence: 99%

HKR For Handwritten Kazakh & Russian Database

Nurseitov,

Bostanbekov,

Kurmankhojayev

et al. 2020

Preprint

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In this paper, we present a new Russian and Kazakh database (with about 95% of Russian and 5% of Kazakh words/sentences respectively) for offline handwriting recognition. A few pre-processing and segmentation procedures have been developed together with the database. The database is written in Cyrillic and shares the same 33 characters. Besides these characters, the Kazakh alphabet also contains 9 additional specific characters. This dataset is a collection of forms. The sources of all the forms in the datasets were generated by L A T E Xwhich subsequently was filled out by persons with their handwriting. The database consists of more than 1400 filled forms. There are approximately 63000 sentences, more than 715699 symbols produced by approximately 200 different writers. It can serve re-This work was done with the support of grant funding for scientific projects of the MES RK No AR05135175 Development and implementation of a system for recognizing handwritten addresses of written correspondence JSC KazPost using machine learning.

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A System for Offline Recognition of Handwritten Characters in Malayalam Script

Cited by 14 publications

References 16 publications

Multiple Classifier System for Offline Malayalam Character Recognition

Multiple Classifier System for Offline Malayalam Character Recognition

Diabetic Foot: “The Next Public Health Crisis for India”

HKR For Handwritten Kazakh & Russian Database

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