A feature fusion based optical character recognition of Bangla characters using support vector machine

Pervin, Mst. Tasnim; Afroge, Shyla; Huq, Aminul

doi:10.1109/eict.2017.8275138

Cited by 5 publications

(1 citation statement)

References 5 publications

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“…Gaur & Yadav (2015) have performed K -means clustering and used SVM classifiers to recognize Hindi characters and obtained a result of 95.86% accuracy. Utilizing SVM and combining Zoning and Gabor filter in feature extraction yields a result of 92.99% recognition rate in classifying Bangla characters (Pervin, Afroge & Huq, 2017).…”

Section: Introductionmentioning

confidence: 99%

Optical character recognition system for Baybayin scripts using support vector machine

Pino

Mendoza

Sambayan

2021

PeerJ Computer Science

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In 2018, the Philippine Congress signed House Bill 1022 declaring the Baybayin script as the Philippines’ national writing system. In this regard, it is highly probable that the Baybayin and Latin scripts would appear in a single document. In this work, we propose a system that discriminates the characters of both scripts. The proposed system considers the normalization of an individual character to identify if it belongs to Baybayin or Latin script and further classify them as to what unit they represent. This gives us four classification problems, namely: (1) Baybayin and Latin script recognition, (2) Baybayin character classification, (3) Latin character classification, and (4) Baybayin diacritical marks classification. To the best of our knowledge, this is the first study that makes use of Support Vector Machine (SVM) for Baybayin script recognition. This work also provides a new dataset for Baybayin, its diacritics, and Latin characters. Classification problems (1) and (4) use binary SVM while (2) and (3) apply the multiclass SVM classification. On average, our numerical experiments yield satisfactory results: (1) has 98.5% accuracy, 98.5% precision, 98.49% recall, and 98.5% F1 Score; (2) has 96.51% accuracy, 95.62% precision, 95.61% recall, and 95.62% F1 Score; (3) has 95.8% accuracy, 95.85% precision, 95.8% recall, and 95.83% F1 Score; and (4) has 100% accuracy, 100% precision, 100% recall, and 100% F1 Score.

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Section: Introductionmentioning

confidence: 99%

Optical character recognition system for Baybayin scripts using support vector machine

Pino

Mendoza

Sambayan

2021

PeerJ Computer Science

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Bangla Handwritten Digit Recognition Using an Improved Deep Convolutional Neural Network Architecture

Saha

Faisal

Rahman

2019

2019 International Conference on Electrical, Computer and Communication Engineering (ECCE)

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Handwritten character recognition is a crucial task because of its abundant applications. The recognition task of Bangla handwritten characters is especially challenging because of the cursive nature of Bangla characters and the presence of compound characters with more than one way of writing. In this paper, a classification model based on the ensembling of several Convolutional Neural Networks (CNN), namely, BanglaNet is proposed to classify Bangla basic characters, compound characters, numerals, and modifiers. Three different models based on the idea of state-of-the-art CNN models like Inception, ResNet, and DenseNet are trained with both augmented and non-augmented inputs. Finally, all these models are averaged or ensembled to get the finishing model. Rigorous experimentation on three benchmark Bangla handwritten characters datasets, namely, CMATERdb, BanglaLekha-Isolated, and Ekush has exhibited significant recognition accuracies compared to some recent CNN-based researches. The top-1 recognition accuracies obtained are 98.40%, 97.65%, 97.32%, and the top-3 accuracies are 99.79%, 99.74%, and 99.56% for CMATERdb, BanglaLekha-Isolated and Ekush datasets respectively.

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Bangla Handwritten Basic Character Recognition Using Deep Convolutional Neural Network

Saha

Faisal

Rahman

2019

2019 Joint 8th International Conference on Informatics, Electronics &Amp; Vision (ICIEV) and 2019 3rd International Conference

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A feature fusion based optical character recognition of Bangla characters using support vector machine

Cited by 5 publications

References 5 publications

Optical character recognition system for Baybayin scripts using support vector machine

Optical character recognition system for Baybayin scripts using support vector machine

Bangla Handwritten Digit Recognition Using an Improved Deep Convolutional Neural Network Architecture

Bangla Handwritten Basic Character Recognition Using Deep Convolutional Neural Network

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