Dzongkha Handwritten Digit Recognition using Machine Learning Techniques

Adhikary, Prottay Kumar; Dadure, Pankaj; Saha, Pradipta; Tawmo,

doi:10.1016/j.procs.2023.01.210

Cited by 4 publications

(2 citation statements)

References 6 publications

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“…In comparison to existing methods for recognizing Bengali handwriting, this approach yielded superior results and was more efficient. Adhikary et al (2023) in their study, the authors of the study aimed to employ machine learning techniques to recognize handwritten Dzongkha digits and explore their potential for identifying and categorizing these numerals. Due to the lack of an existing dataset, the authors manually compiled data using Google Jamboard with a digital pen tablet.…”

Section: Machine Learning-based Handwritten Methodsmentioning

confidence: 99%

A Comprehensive Overview of Handwritten Recognition Techniques: A Survey

Ali¹,

Abdulrazzaq²

2023

Journal of Computer Science

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Deep learning and deep neural networks, particularly ConvolutionalNeural Networks (CNNs), are rapidly growing areas of machine learning and are currently the primary tools used for image analysis and classification applications. Handwriting recognition involves using computer algorithms and software to interpret and recognize handwritten text and drawings and has various applications such as automated handwriting analysis, document digitization, and handwriting-based user interfaces. Many deep learning models have been applied in the field of handwriting recognition and various datasets have been used to evaluate new computer vision techniques. This article provides an overview of the current state-of-the-art approaches and contributions to handwriting recognition using different datasets. Furthermore, the paper explains the most commonly used algorithms for recognizing handwritten characters, words, and numbers. Compares them based on their accuracy. This study covered different aspects and methods of machine learning and Deep Learning (DL) for handwritten recognition that showed different achievements for each.

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Section: Machine Learning-based Handwritten Methodsmentioning

confidence: 99%

A Comprehensive Overview of Handwritten Recognition Techniques: A Survey

Ali¹,

Abdulrazzaq²

2023

Journal of Computer Science

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“…Adhikary et al [22] proposed a study for recognizing handwritten Dzongkha digits using machine learning techniques. Since no existing dataset was available, they manually created one using Google Jamboard and a digital pen tablet.…”

Section: Related Workmentioning

confidence: 99%

KurdSet Handwritten Digits Recognition Based on Different Convolutional Neural Networks Models

Hasen Ali,

Abdulrazzaq

2024

TEM Journal

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Recognition of handwritten digits has garnered significant interest among researchers in the domain of recognizing pattern. This interest stems from the recognition's relevance in various real-life applications, including reading financial checks and official documents, which has remained a persistent obstacle. To address this challenge, researchers have developed numerous algorithms focusing on recognizing handwritten digits across different human languages. This paper presents a new Kurdish Handwritten dataset, consisting of Kurdish characters, digits, texts, and symbols. The dataset consists of 1560 participants, encompassing a broad and varied group. It serves as the primary dataset for training and evaluating algorithms in Kurdish digit recognition. We used Kurdish dataset named (KurdSet) and Arabic dataset for handwritten recognition, which holds 70,000 images of Arabic digits that were written by 700 various participants. Additionally, various models are utilized in the study, including ResNet50, DenseNet121, MobileNet, and a custom CNN (convolutional neural network). Additionally, the models' effectiveness was assessed through the examination of test accuracy, which measures the percentage of correctly classified digits in the evaluation phase. ResNet50 also performs exceptionally well that achieved test accuracy 99.67%, indicating its All models exhibit good performance, DenseNet121 and the Custom CNN Model demonstrate the highest test accuracy of 99.73%, highlighting their superior performance. capabilities in capturing relevant features. Despite its accuracy, MobileNet still exhibits good recognition capability with a test accuracy 99.54%.

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Adaptive Residual Attention Network for Handwritten Character and Digits Recognition with Improved Energy Valley Optimizer Algorithm

Kennedy Babu C

2024

2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems (ADICS)

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Dzongkha Handwritten Digit Recognition using Machine Learning Techniques

Cited by 4 publications

References 6 publications

A Comprehensive Overview of Handwritten Recognition Techniques: A Survey

A Comprehensive Overview of Handwritten Recognition Techniques: A Survey

KurdSet Handwritten Digits Recognition Based on Different Convolutional Neural Networks Models

Adaptive Residual Attention Network for Handwritten Character and Digits Recognition with Improved Energy Valley Optimizer Algorithm

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