Enterprise OCR for real-world documents
OCR that handles Indic scripts, complex layouts, and long PDFs at scale.
Extract text with high reliability from noisy scans, semistructured forms, tables, and handwritten notes. Built for operations teams that need dependable output, not demo-only OCR.
- Indic language support across mixed scripts
- Table extraction with row-column fidelity
- Handwritten and low-quality scan robustness
- Long-PDF processing with stable page coverage
OCR Output Quality Snapshot
Single pipeline for multilingual text, tables, and handwriting.
Indic language text blocks
High-confidence extraction
Semistructured forms
Field-level parse ready
Tables in scanned PDFs
Header + cell mapping retained
Handwritten annotations
Readable text recovery
Capabilities
Built for difficult OCR scenarios
From Indian-language records to image-heavy scans, the engine is tuned for documents where generic OCR pipelines usually degrade.
Indic Language OCR
Handles multilingual pages with script mixing, regional forms, and non-uniform spacing commonly seen in government and legal documents.
Semistructured Documents
Extracts meaningful fields from notices, circulars, orders, and templates without requiring rigid fixed-layout assumptions.
Table Extraction
Recovers table structure with headers and cells intact so downstream analytics and indexing can operate on clean tabular output.
Handwritten Content
Improves readability and capture from handwritten notes, marginal annotations, and mixed print-handwriting pages.
Comparison
Why this outperforms typical OCR and LLM-only extraction
For document-heavy teams, reliability matters more than one-off sample accuracy. This OCR stack is tuned for consistent page-level extraction under messy real-world conditions.
Scenario
Traditional OCR Engines
LLM-only PDF Parsing
Votum OCR
Indic scripts + mixed languages
Lower recall on script variation and noisy glyphs
Can miss tokens when text layer quality is weak
Higher script robustness with cleaner token recovery
Semistructured legal/government forms
Often needs heavy manual template handling
Layout inference can drift across pages
Consistent field capture from variable layouts
Tables in scanned documents
Cell merges and header alignment frequently break
May summarize instead of preserving structure
Row-column mapping retained for downstream systems
Long PDFs
Accuracy drops over long noisy batches
Context limits and chunking can miss coverage
Page-by-page extraction keeps stable full-document coverage
Long PDF Reliability
Extract from 300+ page document sets without brittle chunking.
GPT/Gemini-style parsers are useful for reasoning, but long scanned PDFs can break extraction consistency due to context windows and uneven page quality. This pipeline keeps extraction deterministic and complete at the page level.
Pipeline for Long Document Packs
Multi-language page detection
Table-aware extraction pass
Handwriting recovery pass
Normalized text + coordinates
Controlled storage and auditability
Ready for governed workflows
Workflow
From raw scans to usable data
Production OCR is not just recognition. It is extraction, validation, and structured handoff to teams and systems.
1. Ingest
Upload scans, PDFs, and image bundles from court records, archives, or field teams.
2. Recognize
Detect scripts and extract text blocks, tables, and handwriting with targeted OCR passes.
3. Structure
Generate normalized outputs for search, analytics, and automated document workflows.
4. Govern
Route through approvals and secure storage with audit-friendly records and controls.
Security and Control
OCR for governed, high-stakes document workflows.
Designed for teams that need traceable extraction, strict access controls, and consistent outputs across sensitive records.
Need OCR for challenging documents?
Share sample PDFs and we will walk you through extraction quality, output schema, and deployment options.
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