AI-OCR
Agentic Intelligent OCR & Data Conversion
Perfectly extracts text, tables, and graphs. Analyzes meaning beyond recognition and converts to HTML/Markdown.
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Document Digitization Still Done Manually?
Enterprises hold vast archives of contracts, invoices, and tax documents in paper or PDF form. Basic OCR only extracts text — table structures, graph values, and layout relationships still require manual processing, bottlenecking automation efforts.
AI-OCR That Understands Document Meaning
Beyond text, it accurately extracts table row/column structures, graph values, and intelligently maps Key-Value relationships. Invoice line items, contract clause structures, and form fields are automatically organized and converted to HTML or Markdown.
SUVsoft's AI-OCR
Agentic design enables downstream workflow automation. Recognized data can automatically feed into ERP or CRM systems, or trigger the next AI agent task — enabling true end-to-end document automation from recognition to action.
Key Features
Agentic OCR
Intelligently identifies Key-Value relationships beyond text extraction.
Unstructured Data
High-precision recognition of complex table structures and graphs.
Format Conversion
Converts output to Markdown or HTML for instant use.
Agent Linkage
Integrates with AI agents for follow-up task processing.
Specifications
What it reads
- Document types
- Contracts, invoices, tax documents, certificates
- File formats
- PDF, JPG, PNG, TIFF, BMP
- Tables
- Row/column structure recognised, merged cells handled
- Unstructured
- Graph values and key-value relationships extracted
Accuracy and review
- Korean accuracy
- Over 95% with Korean-specialised models
- Method
- Key-value relationships, not fixed coordinates (ROI)
- Confidence
- Scored per item, lowest reviewed first
- Poor scans
- Preprocessing improves recognition
Output and integration
- Export
- HTML and Markdown
- Systems
- API feed into ERP, CRM, groupware
- Downstream
- Triggers the next AI agent task
- Personal data
- Automatic detection and masking of identifiers
How Deployment Works
- 1
Document typing
We group the documents to be processed and decide which fields matter for each type. This is where the scope of automation gets set.
- 2
Accuracy validation (PoC)
Accuracy is measured per document type on your real samples, so the automated range and the human-review range are known as numbers.
- 3
Tuning
Failure types are analysed and extraction rules and confidence thresholds adjusted.
- 4
Review screen and integration
A review screen shows the original document beside the extracted values, and confirmed entries flow into your existing systems.
- 5
Operation
Review history is used to keep improving the document types that fail most often.
In Practice
“Screening certificates and career records”
Staff move from reading and typing to confirming and approving. Typos and omissions from manual entry disappear structurally.
“Turning invoices into structured data”
Line items and amounts are mapped automatically into ERP. Row-column relationships survive even on forms with merged cells.
“Extracting contract clause structure”
Clause structure and key terms become data you can search and compare.
“Backlogs of paper and PDF documents”
Low-quality scans are handled through preprocessing, and personal data is masked automatically to reduce retention risk.
FAQ
Regular OCR only extracts text, but AI-OCR understands table structures, graph values, and Key-Value relationships semantically. It can automatically map invoice items to amounts and organize contract clause structures.
Over 95% recognition accuracy is achieved using Korean-specialized training models. Technical terminology in medical, legal, and financial domains is also recognized with high precision.
PDF, JPG, PNG, TIFF, BMP, and other major document formats are supported. Low-quality scanned documents are enhanced through preprocessing to improve recognition accuracy.
Yes, extracted data can be automatically fed into ERP, CRM, and groupware systems via API. AI agent integration also enables downstream task automation.
That case is exactly what this approach is built for. Coordinate-based (ROI) templating returns wrong values as soon as a field moves. AI-OCR reads key-value relationships rather than positions, so results stay the same whether a field shifts down the page or wraps onto a second line. We published a validation on real public-sector documents in our case studies.
Row and column structure is recognised alongside the text, so merged cells keep their original row-column relationships in the output. The difference shows most on invoices and financial forms where subtotal rows are merged.
Every extracted item carries a confidence score. The review screen surfaces low-confidence items first, so staff spend time where checking is actually needed rather than scanning everything with equal attention.
Deployed on-premise, neither documents nor extracted results leave your internal network. Automatic detection and masking of national ID numbers and similar identifiers can be applied as part of the pipeline.
Yes. We recommend a PoC that measures accuracy on your real document samples. You see, per document type, how much is automated and where a human still needs to look, and decide from that number.
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