Ge’ez Digit Recognition Model Based on Convolutional Neural Network
Despite the historical significance of the Ge’ez script, there is a notable scarcity of studies on Ge’ez digit recognition, compounded by the challenges posed by the absence of publicly accessible datasets. The complex structure of Ge’ez digits further complicates the recognition task. In response t...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2024-12-01
|
| Series: | Applied Artificial Intelligence |
| Online Access: | https://www.tandfonline.com/doi/10.1080/08839514.2024.2400641 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849220184680693760 |
|---|---|
| author | Ruchika Malhotra Maru Tesfaye Addis |
| author_facet | Ruchika Malhotra Maru Tesfaye Addis |
| author_sort | Ruchika Malhotra |
| collection | DOAJ |
| description | Despite the historical significance of the Ge’ez script, there is a notable scarcity of studies on Ge’ez digit recognition, compounded by the challenges posed by the absence of publicly accessible datasets. The complex structure of Ge’ez digits further complicates the recognition task. In response to this gap, our study addresses the development of a digit recognition model based on deep learning (DL) specifically tailored for printed Ge’ez digits, accompanied by the creation of a comprehensive study dataset. The proposed model architecture seamlessly integrates one input layer, six convolutional layers, and three Max-Pooling layers. To assess the model’s performance, we meticulously curated a Ge’ez digit dataset comprising 72,000 images, well-suited for DL applications using the ocropus-linegen model within OCRopus, a free document analysis tool. Leveraging cutting-edge DL algorithms, our proposed model demonstrates an impressive accuracy of 97.29%. This surpasses the performance of previous Ge’ez digit recognition models, marking a noteworthy advancement in this underexplored domain. |
| format | Article |
| id | doaj-art-2a93fab43b494cfb8cee4d079d84041c |
| institution | Kabale University |
| issn | 0883-9514 1087-6545 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Applied Artificial Intelligence |
| spelling | doaj-art-2a93fab43b494cfb8cee4d079d84041c2024-12-16T16:13:01ZengTaylor & Francis GroupApplied Artificial Intelligence0883-95141087-65452024-12-0138110.1080/08839514.2024.2400641Ge’ez Digit Recognition Model Based on Convolutional Neural NetworkRuchika Malhotra0Maru Tesfaye Addis1Department of Software Engineering, Delhi Technological University, New Delhi, IndiaDepartment of Computer Science, Debre Tabor University, Debre Tabor, EthiopiaDespite the historical significance of the Ge’ez script, there is a notable scarcity of studies on Ge’ez digit recognition, compounded by the challenges posed by the absence of publicly accessible datasets. The complex structure of Ge’ez digits further complicates the recognition task. In response to this gap, our study addresses the development of a digit recognition model based on deep learning (DL) specifically tailored for printed Ge’ez digits, accompanied by the creation of a comprehensive study dataset. The proposed model architecture seamlessly integrates one input layer, six convolutional layers, and three Max-Pooling layers. To assess the model’s performance, we meticulously curated a Ge’ez digit dataset comprising 72,000 images, well-suited for DL applications using the ocropus-linegen model within OCRopus, a free document analysis tool. Leveraging cutting-edge DL algorithms, our proposed model demonstrates an impressive accuracy of 97.29%. This surpasses the performance of previous Ge’ez digit recognition models, marking a noteworthy advancement in this underexplored domain.https://www.tandfonline.com/doi/10.1080/08839514.2024.2400641 |
| spellingShingle | Ruchika Malhotra Maru Tesfaye Addis Ge’ez Digit Recognition Model Based on Convolutional Neural Network Applied Artificial Intelligence |
| title | Ge’ez Digit Recognition Model Based on Convolutional Neural Network |
| title_full | Ge’ez Digit Recognition Model Based on Convolutional Neural Network |
| title_fullStr | Ge’ez Digit Recognition Model Based on Convolutional Neural Network |
| title_full_unstemmed | Ge’ez Digit Recognition Model Based on Convolutional Neural Network |
| title_short | Ge’ez Digit Recognition Model Based on Convolutional Neural Network |
| title_sort | ge ez digit recognition model based on convolutional neural network |
| url | https://www.tandfonline.com/doi/10.1080/08839514.2024.2400641 |
| work_keys_str_mv | AT ruchikamalhotra geezdigitrecognitionmodelbasedonconvolutionalneuralnetwork AT marutesfayeaddis geezdigitrecognitionmodelbasedonconvolutionalneuralnetwork |