A Machine Learning and Deep Learning Approach for Recognizing Handwritten Digits

Sharma, Ayushi; Bhardwaj, Harshit; Bhardwaj, Arpit; Sakalle, Aditi; Acharya, Divya; Ibrahim, Wubshet

doi:10.1155/2022/9869948

Cited by 3 publications

(2 citation statements)

References 17 publications

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“…Testing accuracy they achieved for Incep-tionV3, EfficientNet-b0 and RestNet50 are 88%, 96%, and 94%. The Support Vector Machine, Conditional Random fields, Decision Tree, and K nearest Neighbor, in addition to a CNN, were utilized as just the classification algorithm in the study [18] by Ayushi Sharma et al The CNN they have used outperforms some other classifiers, identifying handwritten characters with a testing accuracy of only 98.83%. The main resource for separating learning seems to be the Standard MNIST dataset [19], which was released by York University.…”

Section: Digit Recognition Based Papersmentioning

confidence: 99%

Evaluation of Bengali Handwritten Mathematical Equationusing Convolutional Neural Network

Khan

Dewanjee

Shorna

et al. 2023

Preprint

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Humans are naturally capable of solving mathematical expressions, but machines lack the abilityto comprehend an issue through a visual context. Computers are gradually becoming moreadvanced and catching up with the subtlety and inaccuracy of real life. The need for an automatedsystem to check answer scripts of mathematical equations has become unparallel, especiallyfor Bengali handwritten scripts. This study checks each line of the solution of a mathematicalequation to evaluate its correctness using a deep learning approach. In contrast to earliermethods, this paper introduces a CNN architecture to verify the accuracy of a handwritten mathematicalequation in addition to solving the problem. The model reads a handwritten equationand validates its mathematical symbols and operations. A dataset has been created to evaluatethe models performance which is named ”BHQED”. The experimental result shows that theaccuracy of the proposed CNN architecture is 92.25% and the recall is 90.65% on our solelycreated dataset. To further boost the performance, this study applies the pretrained ResNet18model and substantially outperforms the CNN with an accuracy of 94.57% and recall of 93.69%.

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Section: Digit Recognition Based Papersmentioning

confidence: 99%

Evaluation of Bengali Handwritten Mathematical Equationusing Convolutional Neural Network

Khan

Dewanjee

Shorna

et al. 2023

Preprint

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“…Document categorization is a crucial task that aims to classify documents into predefined categories to facilitate their management and analysis. Traditional approaches to document categorization or document classification problem have relied on manual classification or rule-based systems, which are time-consuming, laborintensive, and prone to errors [2]. In contrast, deep learning techniques have shown great promise in automating document categorization problems, offering a more efficient, accurate, and scalable solution of the given problem [3].…”

Section: Introductionmentioning

confidence: 99%

Automatic Classification of Scanned Electronic University Documents using Deep Neural Networks with Conv2D Layers

Baimakhanova,

Zhumadillayeva,

Avdarsol

et al. 2023

IJACSA

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This paper proposes a novel approach for scanned document categorization using a deep neural network architecture. The proposed approach leverages the strengths of both convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to extract features from the scanned documents and model the dependencies between words in the documents. The pre-processed documents are first fed into a CNN, which learns and extracts features from the images. The extracted features are then passed through an RNN, which models the sequential nature of the text. The RNN produces a probability distribution over the predefined categories, and the document is classified into the category with the highest probability. The proposed approach is evaluated on a dataset of scanned documents, where each document is categorized into one of four predefined categories. The experimental results demonstrate that the proposed approach achieves high accuracy and outperforms existing methods. The proposed approach achieves an overall accuracy of 97.3%, which is significantly higher than the existing methods' accuracy. Additionally, the proposed approach's performance was robust to variations in the quality of the scanned documents and the OCR accuracy. The contributions of this paper are twofold. Firstly, it proposes a novel approach for scanned document categorization using deep neural networks that leverages the strengths of CNNs and RNNs. Secondly; it demonstrates the effectiveness of the proposed approach on a dataset of scanned documents, highlighting its potential applications in various domains, such as information retrieval, data mining, and document management. The proposed approach can help organizations manage and analyze large volumes of data efficiently.

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Artificial Neural Networks for Data Processing: A Case Study of Image Classification

Ramasamy,

Ravikumar,

Shitharth

2023

Advanced Mathematical Applications in Data Science

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An Artificial Neural Network (ANN) is a data processing paradigm inspired by the way organic nervous systems, such as the brain, process data. The innovative structure of the information processing system is a crucial component of this paradigm. It is made up of a huge number of highly linked processing components (neurons) that work together to solve issues. Neural networks handle data in the same manner that the human brain does. The network is made up of several densely linked processing units (neurons) that operate in parallel to solve a given problem. They are unable to be programmed to execute a specific activity. ANN, like humans, learns by example. Through a learning process, an ANN is trained for a specific application, such as pattern recognition or data categorization. In biological systems, learning includes changes to the synaptic connections that occur between neurons. This is also true for ANNs. Artificial Neural Networks are used for classification, regression, and grouping. Stages of image processing are classified as preprocessing, feature extraction, and classification. It can be utilized later in the process. ANN should be provided with features and output should be classified. This paper provides an overview of Artificial Neural Networks (ANN), their operation, and training. It also explains the application and its benefits. Artificial Neural Network has been used to classify the MNIST dataset.

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A Machine Learning and Deep Learning Approach for Recognizing Handwritten Digits

Cited by 3 publications

References 17 publications

Evaluation of Bengali Handwritten Mathematical Equationusing Convolutional Neural Network

Evaluation of Bengali Handwritten Mathematical Equationusing Convolutional Neural Network

Automatic Classification of Scanned Electronic University Documents using Deep Neural Networks with Conv2D Layers

Artificial Neural Networks for Data Processing: A Case Study of Image Classification

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