Hindi Handwritten Character Recognition using Convolutional Neural Network

Verma, Karishma; Singh, Manjeet

doi:10.26438/ijcse/v6i6.909914

Cited by 7 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The hidden layers refer to a deep neural network that is used for computation of the values inputted in the input layer. The term "deep" is used to refer to the hidden layers of the neural network [25] as shown in Fig. 6.…”

Section: E Deep Neural Networkmentioning

confidence: 99%

Handwriting Recognition Using Artificial Intelligence Neural Network and Image Processing

2023

IRJMETS

View full text Add to dashboard Cite

Due to increased usage of digital technologies in all sectors and in almost all day to day activities to store and pass information, Handwriting character recognition has become a popular subject of research. Handwriting remains relevant, but people still want to have Handwriting copies converted into electronic copies that can be communicated and stored electronically. Handwriting character recognition refers to the computer's ability to detect and interpret intelligible Handwriting input from Handwriting sources such as touch screens, photographs, paper documents, and other sources. Handwriting characters remain complex since different individuals have different handwriting styles. This paper aims to report the development of a Handwriting character recognition system that will be used to read students and lectures Handwriting notes. The development is based on an artificial neural network, which is a field of study in artificial intelligence. Different techniques and methods are used to develop a Handwriting character recognition system. However, few of them focus on neural networks. The use of neural networks for recognizing Handwriting characters is more efficient and robust compared with other computing techniques. The paper also outlines the methodology, design, and architecture of the Handwriting character recognition system and testing and results of the system development. The aim is to demonstrate the effectiveness of neural networks for Handwriting character recognition.

show abstract

Section: E Deep Neural Networkmentioning

confidence: 99%

Handwriting Recognition Using Artificial Intelligence Neural Network and Image Processing

2023

IRJMETS

View full text Add to dashboard Cite

show abstract

“…In the past few years, there have been sizeable developments in the field of handwritten character recognition [24], [30]. However, most of the research in this field deals with the recognition of the 26 alphabets and 10 digits of the Latin languages.…”

Section: Related Work a Urdu Handwritten Character Recognitionmentioning

confidence: 99%

An Enhanced Prototypical Network Architecture for Few-Shot Handwritten Urdu Character Recognition

Sahay

Coustaty

2023

IEEE Access

View full text Add to dashboard Cite

Few shot models have started to gain a lot of popularity in the past few years. This is mostly because these models grant the ability to structure the representation space (classes) using a very less amount of examples for each class. Such models are usually trained on a wide range of different classes and their examples, which allows them to form and learn a decision-based metric in the process. Non-Latin languages, especially languages such as Urdu, have a bi-linear direction of writing and are context-sensitive in nature, and are hard to recognize. Also, unlike traditional English, there is a very small amount of clean, collated, and usable data that is available for the Urdu language. In this paper, we explore a prototypical network for k-shot classification on handwritten Urdu characters. The prototypical network learns the Euclidean embeddings of the provided images and uses clusters to classify newer examples. Our improved method is able to outperform other methods of few-shot learning and is able to accurately classify both Urdu characters as well as numerals using a minimal number of examples. After comprehensive qualitative and quantitative evaluation and comparison of our proposed approach with other methods to classify handwritten text in few-shot settings, we found out that our proposed approach was typically able to beat other methods by a margin of 1% − 2% while relying on a small training set.

show abstract

“…After the image is processed by the CNN layers and the essential features are extracted, the flattened output from the final CNN layer is given to three fully connected layers. The first layer consists of 128 units, with all having Rectified Linear Unit (ReLU) as the activation function [8], [9]. After that, batch normalization is applied.…”

Section: Fully Connected Layersmentioning

confidence: 99%

Offline Handwritten Character Recognition using Convolutional Neural Network

Devi¹

2021

IJRASET

View full text Add to dashboard Cite

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

show abstract

Hindi Handwritten Character Recognition using Convolutional Neural Network

Cited by 7 publications

References 0 publications

Handwriting Recognition Using Artificial Intelligence Neural Network and Image Processing

Handwriting Recognition Using Artificial Intelligence Neural Network and Image Processing

An Enhanced Prototypical Network Architecture for Few-Shot Handwritten Urdu Character Recognition

Offline Handwritten Character Recognition using Convolutional Neural Network

Contact Info

Product

Resources

About