4 A Comparative Analysis of Machine Learning Techniques for Odia Character Recognition

Das, Mamatarani; Panda, Mrutyunjaya; Dash, Shreela

doi:10.1515/9783110610987-006

Cited by 4 publications

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“…The authors of www.ijacsa.thesai.org [10] used zone centroid distance and standard deviation to extract features and got 94% accuracy by back propagation NN with a genetic algorithm approach. [11], [12], [13]- [15], [16], [17] [18] had contributed their work on handwritten Odia handwritten numeral recognition (Odia OHNR). The same BESAC features are used for numeral classification, on the IITBBS numeral dataset [9].…”

Section: Related Workmentioning

confidence: 99%

Enhancing the Odia Handwritten Character and Numeral Recognition System's Performance with an Ensemble of Deep Neural Networks

Das,

Panda,

Sahoo

2024

IJACSA

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Offline handwritten character recognition (OHCR) is considered a challenging task in pattern recognition due to the inter-class similarity and intra-class variations among the symbols present in the alphabet set. In this work, a learningbased weighted average ensemble of deep neural network models (WEnDNN) is proposed to classify the 10 digits and 47 characters present in the alphabet set of Odia language, an official language of India. To build the base model for the ensemble network (EnDNN), three suitable convolutional neural networks (CNN), are designed and trained from scratch. The WEnDNN's accuracy is increased by using a grid search approach to determine the ideal weight allocations to give to the top-performing model. The performance of the WEnDNN model is compared with several standard machine learning models, which take the nonhandcrafted features extracted from the finely tuned, pre-trained VGG16 model and a combination of Gabor and pixel intensity values to create handcrafted features. On several benchmark handwritten datasets, including NITR Odia characters (OHCS v1.0), ISI Kolkata Odia numerals, and IITBBS Odia numerals, the performance of the proposed WEnDNN model is assessed and compared. The experimental results demonstrate that, in terms of recognition accuracy, the proposed approach beats other state-of-the-art approaches.

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Section: Related Workmentioning

confidence: 99%

Enhancing the Odia Handwritten Character and Numeral Recognition System's Performance with an Ensemble of Deep Neural Networks

Das,

Panda,

Sahoo

2024

IJACSA

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“…Handwritten character recognition (HCR), online or ofine, postal-address interpretation, writer recognition, signature verifcation, real-time handwriting recognition, bank-check/cheque processing, or note preparation are only a few of the ongoing study felds where deep learning produces better accuracy. Several studies have been conducted in the domain of optical character recognition in several languages [6,7], but progress in the Odia language has been limited. Te authors of [8] analyze various approaches for handwritten character recognition using a standard handwritten digit recognition test, and convolutional neural networks (CNNs) have been found to outperform all other techniques when dealing with the variability of 2-D shapes.…”

Section: Related Work On Handwritten Character Recognitionmentioning

confidence: 99%

Enhancing the Power of CNN Using Data Augmentation Techniques for Odia Handwritten Character Recognition

Das

Panda

Dash³

2022

Advances in Multimedia

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The performance of any machine learning model largely depends on the type of input data provided. The higher the volume and variety of the data, the better the machine learning models get trained, thereby producing more accurate results. However, it is a challenging task to get high volume of data in some cases containing enough variety. Handwritten character recognition for Odia language is one of them. NITROHCS v1.0 for handwritten Odia characters and the ISI image database for handwritten Odia numerals are the standard Odia language datasets available for the research community. This paper shows the performance of five different machine learning models that uses a convolutional neural network to identify handwritten characters in response to handwritten datasets that are manipulated and expanded using several augmentation techniques to create variation and increase the volume of the data in the given dataset. These models, with the augmentation techniques discussed in the paper, even lead to a further increase in accuracy by approximately 1% across the models. The claims are supported by the results from the experiments done on the proposed convolutional neural network models on standard available Odia character and numeral data set.

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Optical Character Recognition (OCR) in Handwritten Characters Using Convolutional Neural Networks to Assist in Exam Reader System

Lekshmy,

Velmurugan,

Kumari

et al. 2024

2024 2nd International Conference on Advancement in Computation &Amp;amp; Computer Technologies (InCACCT)

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4 A Comparative Analysis of Machine Learning Techniques for Odia Character Recognition

Cited by 4 publications

References 0 publications

Enhancing the Odia Handwritten Character and Numeral Recognition System's Performance with an Ensemble of Deep Neural Networks

Enhancing the Odia Handwritten Character and Numeral Recognition System's Performance with an Ensemble of Deep Neural Networks

Enhancing the Power of CNN Using Data Augmentation Techniques for Odia Handwritten Character Recognition

Optical Character Recognition (OCR) in Handwritten Characters Using Convolutional Neural Networks to Assist in Exam Reader System

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