Enhanced Recognition of Offline Marathi Handwriting via a Self-Additive Attention Mechanism
Bibliographic record
Abstract
Background: Offline Marathi handwriting recognition presents a significant challenge due to the script's complexity and the variability of individual writing styles.Methods: In the present work, an advanced encoder-decoder framework is introduced, wherein a novel selfadditive-attention mechanism is integrated.This model capitalizes on a Convolutional Neural Network paired with a Joint Scale Feature Extractor (CNN-JSE) to discern low-level image features within the IIIT-HW-Dev dataset.Such features are subsequently fed into an encoder model that employs a dual-phase fusion process: initially leveraging a Bidirectional Long Short-Term Memory (BiLSTM) network, followed by the self-additive-attention mechanism to maintain dependencies over extensive sequences.Results: The fusion output, comprising BiLSTM and self-additive attention data, is conveyed to a Connectionist Temporal Classifier (CTC) decoder.This decoder adeptly identifies character sequences within Marathi words.The introduction of self-additive attention alongside BiLSTM is instrumental in preserving dependencies that are both long-range and multi-stage.Performance Evaluation: The efficacy of the proposed system was rigorously evaluated on the multi-author IIIT-HW-Dev dataset.Performance metrics, specifically Character Error Rate (CER) and Word Error Rate (WER), were employed for benchmarking against various established models.Conclusion: The proposed methodology demonstrates a significant enhancement in the recognition of handwritten Marathi text, thus facilitating the conversion of handwritten documents into machine-editable text.The utilization of selfadditive attention within the BiLSTM framework underscores its potential in capturing complex dependencies, setting a new precedent in automated handwriting recognition technologies.Significance: This study not only paves the way for increased accuracy in handwritten text recognition but also contributes a novel approach to the encoding of feature sequences, which may be applicable to a broad range of pattern recognition applications.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".