Automating Document Intelligence with Vision + NLP

Quick Summary

Challenge

An American print tech company wanted to process millions of diverse documents with automated classification and structured data extraction, across both printed and handwritten formats.

Solution

Tatras Data developed a modular IDP platform using computer vision and NLP to classify documents, extract entities, and convert raw content into structured formats.

Result

90%+ classification accuracy.

Tech Stack

AI: Custom NER model PARSeq for OCR | ML: Document clustering Handwritten + printed form recognition | Data & Retrieval: Table extraction Entity labeling Noisy input filtering | Dev: Microservice-based IDP platform Format-specific pipelines | Viz: Structured outputs from unstructured docs | Security: On-premise ready Enterprise-scale deployment

The Challenge

Document processing at scale is messy. This American print tech company operated in 160+ countries; each with its own paperwork standards, layouts, and formats. From receipts to invoices, contracts to claim forms, they needed a way to ingest any document and instantly know what it was, what it said, and what to do with it. Manual review didn’t scale. Off-the-shelf OCR didn’t cut it. They needed a true Intelligent Document Processing (IDP) backbone — one that could classify, extract, and structure data across handwritten and printed formats, reliably.

A Day in the Life: Before Our Solution

Every incoming document meant a new decision tree. First, someone had to guess the type — invoice, contract, delivery receipt? Then came the copy-paste grind: pulling out names, totals, due dates, policy IDs. Handwritten forms added a new layer of complexity. And if the document had tables or multiple layouts? The workflow often broke. Each team built its own macros or manual templates. Errors started creeping in. Deadlines were slipping. The knowledge locked inside documents stayed hidden.

Pain Points:

Manual classification delayed downstream workflows
Standard OCR struggled with handwritten or mixed-layout documents
Tables and forms were inconsistently recognized
Entity tagging required human review
Output needed reformatting before use in any system

Solution

1. Core Innovation

Tatras deployed a microservice-based IDP engine combining computer vision and NLP:

A clustering model first classifies documents by type, layout, and format
PARSeq, a permuted autoregressive model, reads both printed and handwritten text
Custom NER models tag relevant fields — like names, totals, IDs — with high precision
Table recognition converts tabular data into structured, machine-readable format
The entire system is modular, so formats and rules can evolve without retraining the core

From receipts to claim forms, every document now flows into a single intelligent pipeline.

2. Key Features

Auto-classification of printed and handwritten documents
Fine-tuned NER model for entity extraction
Table recognition and structured data output
Microservice architecture for modular scalability
Layout-aware processing across formats

3. Workflow Integration

The IDP system plugs into the company’s existing document intake processes. As soon as a document enters the system, it’s classified, parsed, and converted into structured fields that downstream tools can consume, with no need for human prep. Our system accelerated everything from invoicing to customer onboarding.

Outcomes

📂 90%+ accuracy in document classification

🧾 75%+ precision in entity extraction

📝 Seamless handling of both printed and handwritten inputs

⚙️ Structured data output for downstream automation

🔄 Lower error rates and turnaround time across processes