INTELLIDOC - An Adaptive Transformer-Powered Pipeline For Intelligent Document Processing And Entity Extraction
Bibliographic record
Abstract
Efficient and accurate processing of unstructured document data is crucial for legal, enterprise, and academic applications, where vast amounts of textual information must be extracted, summarized, and analyzed. Traditional Optical Character Recognition (OCR) and Named Entity Recognition (NER) methods often face challenges in handling handwritten text, scanned documents, and complex legal structures, leading to data loss and misclassification. To address these limitations, we propose IntelliDoc, an adaptive, transformer-powered document processing pipeline designed to enhance accuracy, efficiency, and contextual understanding of document intelligence. IntelliDoc employs a hybridized multi-stage pipeline that integrates an adaptive OCR layer, which dynamically adjusts to different document characteristics, ensuring high extraction accuracy for diverse document types. Experimental evaluations on a benchmark dataset comprising legal, financial, and administrative documents demonstrate that IntelliDoc achieves an OCR accuracy of 98.2%, NER precision of 94.7%, and a summarization coherence score of 91.5%, significantly outperforming conventional document processing frameworks. Additionally, the parallel architecture reduces processing time by 35% compared to sequential models, making IntelliDoc suitable for real-time applications. Future work will explore integrating domain-specific large language models to further enhance interpretability and accuracy across specialized document categories.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.006 |
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".