A Bengali Handwritten Vowels Recognition Scheme Based on the Detection of Structural Anatomy of the Characters

Das, Priyanka; Dasgupta, Tanmoy; Bhattacharya, Sayantan

doi:10.1007/978-981-10-3373-5_24

Cited by 2 publications

(3 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The results of the experiment show that the proposed deep hog model can surpass the existing sophisticated taxonomy models in Bengali handwritten alpha numerical image classification (Sharif & Mahboob, 2019). Das et al (2018) introduced a Bengali handwritten recognition scheme for vowels in English characters which is quite fast and it does not need training data to work and based on the structural anatomy of the characters. Some feature acquisition is accomplished by generating curvature scale spaces for characters.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Some feature acquisition is accomplished by generating curvature scale spaces for characters. Binary images of handwritten letters are treated to identify physical characteristics such as 'bowl', 'lobe', and 'arm' (Das et al, 2018). Now a days handwriting character recognition is one of the most important issues in the gift of neural networks and machine learning algorithms in pattern recognition applications (Kamran et al, 2018;Hochuli et al, 2018).…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Handwritten Bangla Numerical Digit Recognition Using Fine Regulated Deep Neural Network

Hossain

Abadin

et al. 2021

Eng. int. (Dhaka)

View full text Add to dashboard Cite

The recognition of handwritten Bangla digit is providing significant progress on optical character recognition (OCR). It is a very critical task due to the similar pattern and alignment of handwriting digits. With the progress of modern research on optical character recognition, it is reducing the complexity of the classification task by several methods, a few problems encounter during recognition and wait to be solved with simpler methods. The modern emerging field of artificial intelligence is the Deep Neural Network, which promises a solid solution to these few handwritten recognition problems. This paper proposed a fine regulated deep neural network (FRDNN) for the handwritten numeric character recognition problem that uses convolutional neural network (CNN) models with regularization parameters which makes the model generalized by preventing the overfitting. This paper applied Traditional Deep Neural Network (TDNN) and Fine regulated deep neural network (FRDNN) models with a similar layer experienced on BanglaLekha-Isolated databases and the classification accuracies for the two models were 96.25% and 96.99%, respectively over 100 epochs. The network performance of the FRDNN model on the BanglaLekha-Isolated digit dataset was more robust and accurate than the TDNN model and depend on experimentation. Our proposed method is obtained a good recognition accuracy compared with other existing available methods.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Handwritten Bangla Numerical Digit Recognition Using Fine Regulated Deep Neural Network

Hossain

Abadin

et al. 2021

Eng. int. (Dhaka)

View full text Add to dashboard Cite

show abstract

“…Researchers have presented different types of feature extraction techniques and proposed some new feature extraction techniques for recognizing handwritten Bengali characters. Since Bengali consists of differ-Ì ISSN: 2252-8938 ent parts such as the upper part, middle part, lower part, and disjunctive part, many researchers have developed anatomical feature extraction techniques [5], [6]. Since the characters can be divided into different zones, researchers have used a zone-based feature extraction method [7].…”

Section: Introductionmentioning

confidence: 99%

A machine learning approach for Bengali handwritten vowel character recognition

Ahsan

Nawaz

Sarwar

et al. 2022

IJ-AI

View full text Add to dashboard Cite

Recognition of handwritten characters is complex because of the different shapes and numbers of characters. Many handwritten character recognition strategies have been proposed for both English and other major dialects. Bengali is generally considered the fifth most spoken local language in the world. It is the official and most widely spoken language of Bangladesh and the second most widely spoken among the 22 posted dialects of India. To improve the recognition of handwritten Bengali characters, we developed a different approach in this study using face mapping. It is quite effective in distinguishing different characters. The real highlight is that the recognition results are more efficient than expected with a simple machine learning technique. The proposed method uses the Python library Scikit-Learn, including NumPy, Pandas, Matplotlib, and support vector machine (SVM) classifier. The proposed model uses a dataset derived from the BanglaLekha isolated dataset for the training and testing part. The new approach shows positive results and looks promising. It showed accuracy up to 94% for a particular character and 91% on average for all characters.

show abstract

A Bengali Handwritten Vowels Recognition Scheme Based on the Detection of Structural Anatomy of the Characters

Cited by 2 publications

References 6 publications

Handwritten Bangla Numerical Digit Recognition Using Fine Regulated Deep Neural Network

Handwritten Bangla Numerical Digit Recognition Using Fine Regulated Deep Neural Network

A machine learning approach for Bengali handwritten vowel character recognition

Contact Info

Product

Resources

About