BonoNet: a deep convolutional neural network for recognizing bangla compound characters
| Dublin Core | PKP Metadata Items | Metadata for this Document | |
| 1. | Title | Title of document | BonoNet: a deep convolutional neural network for recognizing bangla compound characters |
| 2. | Creator | Author's name, affiliation, country | Kazi Rifat Ahmed; Daffodil International University; Bangladesh |
| 2. | Creator | Author's name, affiliation, country | Nusrat Jahan; Daffodil International University; Bangladesh |
| 2. | Creator | Author's name, affiliation, country | Adiba Masud; Daffodil International University; Bangladesh |
| 2. | Creator | Author's name, affiliation, country | Nusrat Tasnim; Bangladesh University of Professionals; Bangladesh |
| 2. | Creator | Author's name, affiliation, country | Sazia Sharmin; American International University; Bangladesh |
| 2. | Creator | Author's name, affiliation, country | Nusrat Jahan Mim; Daffodil International University; Bangladesh |
| 2. | Creator | Author's name, affiliation, country | Imran Mahmud; Daffodil International University; Bangladesh |
| 3. | Subject | Discipline(s) | |
| 3. | Subject | Keyword(s) | Bangla; BonoNet; Compound characters; Deep convolutional neural network; Handwritten; Optical character recognition |
| 4. | Description | Abstract | The bangla alphabet includes vowels, consonants, and compound symbols. The compound nature of bangla is a product of combining two or more root bangla characters into one graph. They are difficult to differentiate because they have a sophisticated geometric shape and an immense variety of scripts used by different places and individuals. This is one of the greatest challenges in creating effective optical character recognition (OCR) systems for bangla. In this paper, a deep convolutional neural network (DCNN)-based system is presented to identify bangla compound characters with high precision. The model was trained using the AIBangla dataset. It has about 171 classes of bangla compound characters. A DCNN system, BonoNet, was designed to classify compound characters. BonoNet outperformed all other state-of-the-art architecture on the test set and improved over current state-of-the-art architecture methods. BonoNet will greatly improve the automation and analysis of the bangla language by accurately identifying these compound complex characters. |
| 5. | Publisher | Organizing agency, location | Institute of Advanced Engineering and Science |
| 6. | Contributor | Sponsor(s) | |
| 7. | Date | (YYYY-MM-DD) | 2025-10-01 |
| 8. | Type | Status & genre | Peer-reviewed Article |
| 8. | Type | Type | |
| 9. | Format | File format | |
| 10. | Identifier | Uniform Resource Identifier | https://ijai.iaescore.com/index.php/IJAI/article/view/26734 |
| 10. | Identifier | Digital Object Identifier (DOI) | http://doi.org/10.11591/ijai.v14.i5.pp4171-4180 |
| 11. | Source | Title; vol., no. (year) | IAES International Journal of Artificial Intelligence (IJ-AI); Vol 14, No 5: October 2025 |
| 12. | Language | English=en | en |
| 14. | Coverage | Geo-spatial location, chronological period, research sample (gender, age, etc.) | |
| 15. | Rights | Copyright and permissions |
Copyright (c) 2025 Kazi Rifat Ahmed, Nusrat Jahan, Adiba Masud, Nusrat Tasnim, Sazia Sharmin, Nusrat Jahan Mim, Imran Mahmud This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. |