Multiscale Residual Network for Recognizing Handwritten Malayalam Characters
Bibliographic record
Abstract
In domains such as banking cheque processing and automated mail sorting, the recognition of handwritten characters is of paramount importance.In Kerala, where Malayalam serves as the primary language for government documentation, the accurate identification of its handwritten characters is crucial.This study introduces a novel approach leveraging a deep residual neural network with multiscale feature extraction for the recognition of Malayalam handwritten characters, encompassing both basic and compound characters as well as signs.Traditional methods of character recognition often rely on handcrafted feature extraction, which, while achieving commendable accuracy, are prone to misclassification due to reliance on low-and mid-level features in the output layer classifier without consideration of parameter modifications.The proposed method addresses these limitations by integrating multiscale features, enhancing the model's ability to discern intricate character details.Evaluated using the P-ARTS Kayyezhuthu Dataset, this approach demonstrated a remarkable accuracy of 99.56%.Additionally, a commendable accuracy of 98% was achieved on other test datasets.The findings underscore the efficacy of deep learning techniques over conventional methods in handwritten character recognition (HCR), particularly in the context of the complex Malayalam script.This study contributes significantly to the field of machine learning and handwriting analysis, offering robust solutions for applications requiring high precision in character recognition.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".