Two Decades of Bengali Handwritten Digit Recognition: A Survey

Rahman, Asif; Hasan, Md. Bakhtiar; Ahmed, Sharmin; Ahmed, Tasnim; Ashmafee, Md. Hamjajul; Kabir, Mohammad Ridwan; Kabir, Md. Hasanul

doi:10.1109/access.2022.3202893

Cited by 18 publications

(3 citation statements)

References 191 publications

(449 reference statements)

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“…They reported the highest accuracy of 97.09% [35]. An extensive review has been conducted in [36] for offline Bengali handwritten digit recognition and presented a comprehensive insight of state-of-the-art datasets and approaches based on image processing, traditional Machine Learning (ML) and Deep Learning (DL) architectures including several real-life applications. Shukla and Desai proposed a deep learning approach for the recognition of Handwritten Gujarati Characters and Numerals.…”

Section: B Deep Learning Approachesmentioning

confidence: 99%

Handwritten Gujarati Numerals Classification Based on Deep Convolution Neural Networks Using Transfer Learning Scenarios

Goel

Ganatra

2023

IEEE Access

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In recent years, handwritten numeral classification has achieved remarkable attention in the field of computer vision. Handwritten numbers are difficult to recognize due to the different writing styles of individuals. In a multilingual country like India, negligible research attempts have been carried out for handwritten Gujarati numerals recognition using deep learning techniques compared to the other regional scripts. The Gujarati digit dataset is not available publicly and deep learning requires a large amount of labeled data for the training of the models. If the number of annotated data is not sufficient enough to train Convolutional Neural Networks (CNN) from the scratch, transfer learning can be applied. However, the issue arises by using transfer learning is that how deep to fine-tune the pre-trained convolutional neural network while training the target model. In this paper, we addressed these problems using three deep transfer learning scenarios to classify handwritten Gujarati numerals from the images of zero to nine. We presented transfer learning scenarios using ten pre-trained CNN architectures including LeNet, VGG16, InceptionV3, ResNet50, Xception, ResNet101, MobileNet, MobileNetV2, DenseNet169 and EfficientNetV2S to find the best performing model by freezing and fine-tuning the weight parameters. We implemented the pretrained models using a self-created handwritten Gujarati digit dataset with 8000 images of zero to nine digits with data augmentation. Exhaustive experiments are performed using various performance evaluation matrices. EfficientNetV2S model showed promising results among all the models including three transfer learning scenarios and achieved 98.39% training accuracy, 97.92% testing accuracy, 97.69% f1-score, and 97.15% AUC. Our handwritten Gujarati digit dataset is available on https://github.com/Parth-Goel/gujaratihandwritten-digit-dataset/.

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Section: B Deep Learning Approachesmentioning

confidence: 99%

Handwritten Gujarati Numerals Classification Based on Deep Convolution Neural Networks Using Transfer Learning Scenarios

Goel

Ganatra

2023

IEEE Access

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“…Yet, developments in deep neural networks have made it possible to reuse architectures trained to extract features from training data of one domain to extract features from another domain. This method of propagating knowledge, also known as Transfer Learning, has increased learning performance while reducing the computational requirements for training models from scratch [31].…”

Section: Feature Extractionmentioning

confidence: 99%

An Efficient Transfer Learning-based Approach for Apple Leaf Disease Classification

Ashmafee

Ahmed

et al. 2023

2023 International Conference on Electrical, Computer and Communication Engineering (ECCE)

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Correct identification and categorization of plant diseases are crucial for ensuring the safety of the global food supply and the overall financial success of stakeholders. In this regard, a wide range of solutions has been made available by introducing deep learning-based classification systems for different staple crops. Despite being one of the most important commercial crops in many parts of the globe, research proposing a smart solution for automatically classifying apple leaf diseases remains relatively unexplored. This study presents a technique for identifying apple leaf diseases based on transfer learning. The system extracts features using a pretrained EfficientNetV2S architecture and passes to a classifier block for effective prediction. The class imbalance issues are tackled by utilizing runtime data augmentation. The effect of various hyperparameters, such as input resolution, learning rate, number of epochs, etc., has been investigated carefully. The competence of the proposed pipeline has been evaluated on the apple leaf disease subset from the publicly available 'PlantVillage' dataset, where it achieved an accuracy of 99.21%, outperforming the existing works.

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“…Handwritten numeral recognition plays a crucial role in the fields of image processing and computer vision, making significant contributions to diverse applications. This technology is widely used in a wide range of domains, including bank check processing, postal code recognition, and the analysis of historical handwritten documents [1][2][3][4][5]. Accurate recognition of handwritten numerals is crucial in delicate systems like medical records and biometric identification, where mistakes can lead to serious consequences [6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Advancing Multilingual Handwritten Numeral Recognition With Attention-Driven Transfer Learning

Fateh,

Birgani,

Fateh

et al. 2024

IEEE Access

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As deep learning continues to evolve, we have observed huge breakthroughs in the fields of medical imaging, video and frame generation, optical character recognition (OCR), and other domains. In the field of data analysis and document processing, the recognition of handwritten numerals plays a crucial role. This work has led to remarkable changes in OCR, historical handwritten document analysis, and postal automation. In this study, we present a novel framework to overcome this challenge, going beyond digit recognition in only one language. Unlike common methods that focus on a limited set of languages, our method provides a comprehensive solution for recognition of handwritten digit images in 12 different languages. These specific languages are chosen because most of them have fairly distant representations in latent space. We utilize transfer learning, as it reduces the computational cost and maintains the quality of enhanced images and the models' recognition accuracy. Another strength of our approach is the innovative attention-based module called the MRA module. Our experiments confirm that by applying this module, major progress is made in both image quality and the accuracy of handwritten digit recognition. Notably, we reached high precisions, surpassing nearly 2% improvement in specific languages compared to earlier techniques. In this work, we present a robust and cost-effective approach that handles multilingual handwritten numeral recognition across a wide range of languages. The code and further implementation details are available at https://github.com/CVLab-SHUT/HandWrittenDigitRecognition

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Two Decades of Bengali Handwritten Digit Recognition: A Survey

Cited by 18 publications

References 191 publications

Handwritten Gujarati Numerals Classification Based on Deep Convolution Neural Networks Using Transfer Learning Scenarios

Handwritten Gujarati Numerals Classification Based on Deep Convolution Neural Networks Using Transfer Learning Scenarios

An Efficient Transfer Learning-based Approach for Apple Leaf Disease Classification

Advancing Multilingual Handwritten Numeral Recognition With Attention-Driven Transfer Learning

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