Automatic Hindi OCR Error Correction Using MLM-BERT
Bibliographic record
Abstract
Optical Character Recognition (OCR) systems find it challenging to generate accurate text for highly inflectional Indic languages such as Hindi.Inflectional languages possess an extensive vocabulary.Words in these languages can assume different forms based on factors like gender, meaning, or other contextual cues.To enhance the accuracy of OCR and correct the errors resulting from the inflectional nature of language, it is crucial to perform post-processing on output of the OCR.This work focuses on correcting errors in the OCR output specifically for the Hindi language.To overcome existing challenges, an error correction model has been proposed in this work that uses the Masked-Language Modeling with BERT (MLM-BERT).It utilizes the context to provide accurate word suggestions for the incorrect word or masked word.The proposed model has been tested using the Hindi OCR test dataset from IIITH.It achieved an improvement of 3.58% word accuracy over the baseline OCR word accuracy, which demonstrates its effectiveness in enhancing the accuracy of the OCR output text.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.005 |
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".