scholarly journals Convolutional neural network-based ensemble methods to recognize Bangla handwritten character

2021 ◽  
Vol 7 ◽  
pp. e565
Author(s):  
Mir Moynuddin Ahmed Shibly ◽  
Tahmina Akter Tisha ◽  
Tanzina Akter Tani ◽  
Shamim Ripon
Keyword(s):  
Character Recognition ◽  
Autonomous System ◽  
Large Scale ◽  
Ensemble Methods ◽  
Office Automation ◽  
Machine Learning Algorithms ◽  
Handwritten Character ◽  
Handwritten Text ◽  
Feature Extractor ◽  
Handwritten Recognition

In this era of advancements in deep learning, an autonomous system that recognizes handwritten characters and texts can be eventually integrated with the software to provide better user experience. Like other languages, Bangla handwritten text extraction also has various applications such as post-office automation, signboard recognition, and many more. A large-scale and efficient isolated Bangla handwritten character classifier can be the first building block to create such a system. This study aims to classify the handwritten Bangla characters. The proposed methods of this study are divided into three phases. In the first phase, seven convolutional neural networks i.e., CNN-based architectures are created. After that, the best performing CNN model is identified, and it is used as a feature extractor. Classifiers are then obtained by using shallow machine learning algorithms. In the last phase, five ensemble methods have been used to achieve better performance in the classification task. To systematically assess the outcomes of this study, a comparative analysis of the performances has also been carried out. Among all the methods, the stacked generalization ensemble method has achieved better performance than the other implemented methods. It has obtained accuracy, precision, and recall of 98.68%, 98.69%, and 98.68%, respectively on the Ekush dataset. Moreover, the use of CNN architectures and ensemble methods in large-scale Bangla handwritten character recognition has also been justified by obtaining consistent results on the BanglaLekha-Isolated dataset. Such efficient systems can move the handwritten recognition to the next level so that the handwriting can easily be automated.

scholarly journals Recognition of Handwritten Characters using Deep Convolutional Neural Network

2019 ◽  
Vol 8 (6S4) ◽  
pp. 314-317
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Recognition Rate ◽  
Writing Style ◽  
Handwritten Character Recognition ◽  
New Approach ◽  
Optical Character ◽  
Handwritten Character ◽  
Handwritten Recognition ◽  
Difficult Part

Handwritten character recognition (HCR) mainly entails optical character recognition. However, HCR involves in formatting and segmentation of the input. HCR is still an active area of research due to the fact that numerous verification in writing style, shape, size to individuals. The main difficult part of Indian handwritten recognition has overlapping between characters. These overlapping shaped characters are difficult to recognize that may lead to low recognition rate. These factors also increase the complexity of handwritten character recognition. This paper proposes a new approach to identify handwritten characters for Telugu language using Deep Learning (DL). The proposed work can be enhance the recognition rate of individual characters. The proposed approach recognizes with overall accuracy is 94%.

scholarly journals Offline Handwritten Character Recognition using Convolutional Neural Network

2021 ◽  
Vol 9 (8) ◽  
pp. 1483-1489
Author(s):  
N. Devi
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Character Recognition ◽  
Recognition Accuracy ◽  
Probability Score ◽  
Convolution Operation ◽  
Handwritten Character ◽  
Handwritten Text ◽  
Proposed Model ◽  
Fully Connected

Abstract: This paper focuses on the task of recognizing handwritten Hindi characters using a Convolutional Neural Network (CNN) based. The recognized characters can then be stored digitally in the computer or used for other purposes. The dataset used is obtained from the UC Irvine Machine Learning Repository which contains 92,000 images divided into training (80%) and test set (20%). It contains different forms of handwritten Devanagari characters written by different individuals which can be used to train and test handwritten text recognizers. It contains four CNN layers followed by three fully connected layers for recognition. Grayscale handwritten character images are used as input. Filters are applied on the images to extract different features at each layer. This is done by the Convolution operation. The two other main operations involved are Pooling and Flattening. The output of the CNN layers is fed to the fully connected layers. Finally, the chance or probability score of each character is determined and the character with the highest probability score is shown as the output. A recognition accuracy of 98.94% is obtained. Similar models exist for the purpose, but the proposed model achieved a better performance and accuracy than some of the earlier models. Keywords: Devanagari characters, Convolutional Neural Networks, Image Processing

Transfer learning based handwritten character recognition of tamil script using inception-V3 Model

2021 ◽  
pp. 1-12
Author(s):  
R. Gayathri ◽  
R. Babitha Lincy
Keyword(s):  
Transfer Learning ◽  
Character Recognition ◽  
Handwritten Character Recognition ◽  
Handwritten Character ◽  
Adequate Accuracy ◽  
Suitable Arrangement ◽  
Excellent Method ◽  
The Subject ◽  
And Performance ◽  
Handwritten Recognition

The paper describes the excellent method to get first-rate accuracy and performance in the discipline of Tamil character recognition in a handwritten mode. However, the subject is still at a nascent stage and grossly lacks adequate accuracy in the Tamil language, even though several studies have been conducted within the discipline of handwritten character recognition. This paper draws the attention to the offline handwritten recognition for the Tamil language using the Inception-v3 based transfer learning method. The proposed work is conducted on the readily available HP Tamil handwritten character offline dataset (Hewlett-Packard Lab: hpl-tamil-iso-char-offline-1.0.). It reveals that with the suitable arrangement of transfer learning approach with Inception-v3, the pre-trained model can achieve the recognition accuracy of 93.1%, overtaking the former deep learning designs. The achieved accuracy is due to the use of a pre-trained version with transfer learning that regularly hastens the method of the training process on a new task. Overall, this results in higher accuracy and a more capable version.

scholarly journals Pengenalan Karakter Tulisan Tangan Dengan K-Support Vector Nearest Neighbor

2019 ◽  
Vol 9 (1) ◽  
pp. 33
Author(s):  
Aditya Surya Wijaya ◽  
Nurul Chamidah ◽  
Mayanda Mega Santoni
Keyword(s):  
Character Recognition ◽  
Nearest Neighbor ◽  
Recognition Accuracy ◽  
Training Data ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Zone Method ◽  
Handwritten Character ◽  
Testing Data ◽  
Handwritten Recognition

Handwritten characters are difficult to be recognized by machine because people had various own writing style. This research recognizes handwritten character pattern of numbers and alphabet using K-Nearest Neighbour (KNN) algorithm. Handwritten recognition process is worked by preprocessing handwritten image, segmentation to obtain separate single characters, feature extraction, and classification. Features extraction is done by utilizing Zone method that will be used for classification by splitting this features data to training data and testing data. Training data from extracted features reduced by K-Support Vector Nearest Neighbor (K-SVNN) and for recognizing handwritten pattern from testing data, we used K-Nearest Neighbor (KNN). Testing result shows that reducing training data using K-SVNN able to improve handwritten character recognition accuracy.

scholarly journals HANDWRITTEN CHARACTER RECOGNITION USING CONVOLUTIONAL NEURAL NETWORKS

2021 ◽  
Vol 10 (8) ◽  
pp. 41-45
Author(s):  
D.J. Samatha Naidu ◽  
T. Mahammad Rafi
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Character Recognition ◽  
Large Scale ◽  
Deep Neural Networks ◽  
Recognition System ◽  
Self Organizing Map ◽  
Handwritten Character Recognition ◽  
Handwritten Character ◽  
The Neural Network

Handwritten character Recognition is one of the active area of research where deep neural networks are been utilized. Handwritten character Recognition is a challenging task because of many reasons. The Primary reason is different people have different styles of handwriting. The secondary reason is there are lot of characters like capital letters, small letters & special symbols. In existing were immense research going on the field of handwritten character recognition system has been design using fuzzy logic and created on VLSI(very large scale integrated)structure. To Recognize the tamil characters they have use neural networks with the Kohonen self-organizing map(SOM) which is an unsupervised neural networks. In proposed system this project design a image segmentation based hand written character recognition system. The convolutional neural network is the current state of neural network which has wide application in fields like image, video recognition. The system easily identify or easily recognize text in English languages and letters, digits. By using Open cv for performing image processing and having tensor flow for training the neural network. To develop this concept proposing the innovative method for offline handwritten characters. detection using deep neural networks using python programming language.

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