Bangla Handwritten Character Recognition Using Deep Learning Approaches and its Explainability With AI
Why this work is in the frame
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Bibliographic record
Abstract
The realm of Bangla handwritten character recognition (BHCR) has long been overshadowed by the dominance of more mainstream languages, despite Bangla's status as Deep learning (DL) approaches have led to substantial improvements in handwritten character recognition (BHCR) for Bangla, one of the humanity's frequently spoken languages. These methods generally an optimal fit for BHCR because they are good at selecting high-level characteristics from intricate information. In our comprehensive study, we meticulously explored the efficacy of twelve DL models on the arduous task of Bangla character recognition, meticulously evaluating their performance on two distinct datasets: a handwritten character dataset and CMATERDB [1], comprising a formidable collection of 15,000 images. Additionally, we provided an audit of the DL models' achievements for BHCR. Among the compared models are LSTM, Bi-LSTM, CNN, Inception, VGG, and ResNet. we achieved the maximum performance at ResNet152V2. In this study, One of the most exquisite identification rates for Bengali character recognition currently available has been demonstrated by the suggested technique, which displayed an adequate 98.76% recognition accuracy on the dataset.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it