Comparative Study on Handwritten Digit Recognition Classifier Using CNN and Machine Learning Algorithms

Kumari, Tanuja; Vardan, Yatharth; Shambharkar, Prashant Giridhar; Gandhi, Yash

doi:10.1109/iccmc53470.2022.9753756

Cited by 11 publications

(5 citation statements)

References 16 publications

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“…These networks process images as objects in three-dimensional space, incorporating the notion of three-dimensional objects. The influence of CNN on computer vision has undergone a revolutionary transformation [14]. Handwriting recognition is essential in machine learning and has many applications.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Handwritten Bengali Alphabets, Compound Characters and Numerals Recognition Using CNN-based Approach

Asraful,

Hossain,

Hossen

2023

AETiC

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Accurately classifying user-independent handwritten Bengali characters and numerals presents a formidable challenge in their recognition. This task becomes more complicated due to the inclusion of numerous complex-shaped compound characters and the fact that different authors employ diverse writing styles. Researchers have recently conducted significant researches using individual approaches to recognize handwritten Bangla digits, alphabets, and slightly compound characters. To address this, we propose a straightforward and lightweight convolutional neural network (CNN) framework to accurately categorize handwritten Bangla simple characters, compound characters, and numerals. The suggested approach exhibits outperformance in terms of performance when compared too many previously developed procedures, with faster execution times and requiring fewer epochs. Furthermore, this model applies to more than three datasets. Our proposed CNN-based model has achieved impressive validation accuracies on three datasets. Specifically, for the BanglaLekha isolated dataset, which includes 84-character classes, the validation accuracy was 92.48%. On the Ekush dataset, which includes 60-character classes, the model achieved a validation accuracy of 97.24%, while on the customized dataset, which includes 50-character classes, the validation accuracy was 97.03%. Our model has demonstrated high accuracy and outperformed several prominent existing frameworks.

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Section: Convolutional Neural Networkmentioning

confidence: 99%

Handwritten Bengali Alphabets, Compound Characters and Numerals Recognition Using CNN-based Approach

Asraful,

Hossain,

Hossen

2023

AETiC

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“…The MNIST database showed that the proposed method was effective in handwritten digit recognition. In [13], Tanuja et al utilized the CNN, KNN, and SVM to recognize isolated handwritten digits. By implementing and training these models on the same dataset and comparing the results, they discovered that the CNN is the most optimal machine learning technique for classifying handwritten digits, exhibiting a remarkable 99.59% accuracy.…”

Section: Related Workmentioning

confidence: 99%

“…CNNs demonstrate an excellent performance in image identification applications, including handwritten digit recognition [5][6][7][11][12][13][14]18,21,23,27,28,[34][35][36]. A simple convolutional layer achieves over 99% validation accuracy on the MNIST dataset [3][4][5][6][7][8][9].…”

Section: Cnn-based Engraved-digit Recognition Modelmentioning

confidence: 99%

“…We evaluated the experimental setup using the standard metrics commonly used for classifiers: accuracy, precision, recall, and F1 score [8,13,49]. Table 1 shows that the KNN algorithm achieved 26% accuracy in the first experiment set and 79.2 and 91.6% accuracies in the second and third experiment sets, respectively.…”

Section: Evaluation and Analysismentioning

confidence: 99%

“…Developing or identifying the best-performing known models for an unusual dataset type (e.g., engraved digit image) might be necessary when considering the unique data features, data insufficiency, and different types of recognition problems, among others. Accordingly, we develop herein our own CNN-ED model and evaluate it with several CNN-based models for our image recognition task considering their ability to learn complex image representation through multiple layers [11][12][13][14]. Our proposed CNN-ED model achieves an outstanding 99.88% accuracy on the test data, surpassing the 99.77% accuracy of an adapted CNN architecture and outperforming the 99.56% benchmark accuracy achieved by classifiers trained on the MNIST dataset [4,5,[15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

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Effective Digital Technology Enabling Automatic Recognition of Special-Type Marking of Expiry Dates

Abdulraheem,

Jung

2023

Sustainability

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In this study, we present a machine-learning-based approach that focuses on the automatic retrieval of engraved expiry dates. We leverage generative adversarial networks by augmenting the dataset to enhance the classifier performance and propose a suitable convolutional neural network (CNN) model for this dataset referred to herein as the CNN for engraved digit (CNN-ED) model. Our evaluation encompasses a diverse range of supervised classifiers, including classic and deep learning models. Our proposed CNN-ED model remarkably achieves an exceptional accuracy, reaching a 99.88% peak with perfect precision for all digits. Our new model outperforms other CNN-based models in accuracy and precision. This work offers valuable insights into engraved digit recognition and provides potential implications for designing more accurate and efficient recognition models in various applications.

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Comparison of Machine Learning Models for Multiple Hand-Written Digits Recognition

Priyaa,

Thangam,

Kalaivani

et al. 2023

2023 Second International Conference on Advances in Computational Intelligence and Communication (ICACIC)

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Comparative Study on Handwritten Digit Recognition Classifier Using CNN and Machine Learning Algorithms

Cited by 11 publications

References 16 publications

Handwritten Bengali Alphabets, Compound Characters and Numerals Recognition Using CNN-based Approach

Handwritten Bengali Alphabets, Compound Characters and Numerals Recognition Using CNN-based Approach

Effective Digital Technology Enabling Automatic Recognition of Special-Type Marking of Expiry Dates

Comparison of Machine Learning Models for Multiple Hand-Written Digits Recognition

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