From raw scan to AI-ready text.
Four-stage pipeline with AI-assisted quality control. No shortcuts. No unverified data.
The compounding advantage
A cycle, not a funnel.
Every batch of reviewer corrections feeds back into training. Over time the model needs less human review — even as the domain expands and new document formats are introduced.
Document Analysis
Pages are preprocessed — deskewed, denoised, and normalized — then layout analysis segments each page into its regions: headers, body text lines, tables, stamps, and footers.
Shirorekha-aware detection built for Devanagari script handles the quirks of Marathi documents across scanned, legacy-encoded, and natively digital sources.
OCR
A vision language model fine-tuned for Marathi reads each segmented region with full domain context — script, layout, and terminology understood together.
The model extracts text region-by-region, aware of Devanagari script variants, government formatting conventions, and domain-specific vocabulary.
Output
Extracted text is structured, entity-tagged, and published as a searchable database — with full traceability back to the source scan.
Every record retains links to source page images, bounding boxes, and confidence scores, so every downstream datapoint is auditable.
Feedback Loop
Uncertain or conflicting outputs are routed to trained Marathi-fluent reviewers. Their corrections feed back into the training pipeline, making the next iteration of the model stronger.
Human consensus review closes the loop: every correction compounds into better accuracy, steadily reducing the volume of lines that need review.
Data quality is the single biggest lever. This pipeline delivers it.
Verified, human-reviewed training data is what separates generic AI from domain-tuned systems that actually work on Marathi.