AI-Powered Document
Extraction System
Transform PDFs and images into structured, usable data with an experimental pipeline built on OCR, AI extraction, and schema-driven validation.
DocPipeline is a technical showcase of a modern document-processing system designed to convert unstructured business documents into clean, structured outputs for downstream workflows.
Experimental system built for research, testing, and demonstration.
From document to data
Extract structured fields from receipts, utility bills, packing slips, quotes, and more.
Designed for real workflows
Generate outputs that are easier to review, export, and integrate into other systems.
Built as a modern pipeline
Combines OCR, extraction, validation, and integrations in a single end-to-end system.
What This System Does
A document pipeline,
not just OCR
Traditional OCR returns raw text. DocPipeline extends this by transforming document content into structured fields that can be reviewed, validated, and used in downstream workflows.
The system supports multiple document types, normalizes outputs into consistent schemas, and provides a clearer path from document ingestion to usable data.
FRESH MART SUPERMARKET 123 Market St Austin TX 03/07/2026 14:32 Trans #4891 Organic Whole Milk 2x $5.98 Whole Wheat Bread 1x $3.49 Subtotal $38.24 Tax $3.06 TOTAL $44.39 VISA ****4521
Requires manual parsing to extract any value
Ready to export, integrate, or automate
How It Works
A streamlined pipeline from input to output
A streamlined pipeline designed to move from raw document input to structured output.
Upload a document
Start with a PDF or image — a receipt, purchase order, packing slip, expense report, or utility bill.
Extract text and layout
A pluggable OCR layer processes the document and captures text, layout, and positional data.
Identify structured fields
Extraction logic identifies key values — names, dates, totals, tracking numbers, and line items.
Return structured results
Results are organized into structured formats for review and export — raw text, extracted fields, and downloadable files.
Supported Document Types
Built to handle a range of business documents
The system is designed to handle a range of business-oriented document formats, each with its own extraction schema and field mappings.
Receipts
Extract merchant details, totals, taxes, and line items from grocery, restaurant, and retail receipts.
Purchase Orders
Capture supplier information, order details, and structured item data.
Packing Slips
Identify shipping details, tracking numbers, and shipment contents.
Utility Bills
Extract provider information, due dates, and billing amounts.
Expense Reports
Parse summary amounts, vendors, categories, and supporting entries.
Quotes & Estimates
Capture pricing details, vendors, and itemized cost structures.
Architecture
Built as a modular document-processing system
DocPipeline is designed as a multi-stage pipeline rather than a single OCR call. Each layer is separated to allow flexibility, extensibility, and improved reliability across different document types.
This architecture enables improvements at each layer without requiring changes to the entire system.
- Pluggable OCR — swap PaddleOCR or Azure DI without changing extraction logic
- Queue-based processing supports single files and bulk batches
- Type-specific schemas normalize outputs across all document types
- Async delivery routes results to any configured integration
Document Input
PDFs and images are uploaded and prepared for processing.
OCR Layer
A pluggable OCR stage extracts text and layout information from the document.
Extraction Layer
Field extraction logic identifies structured values across different document types.
Validation & Shaping
Outputs are normalized into consistent schemas for easier consumption.
Exports & Integrations
Structured results can be reviewed, downloaded, or sent to downstream systems.
Integrations
Built for downstream workflows
Structured data becomes more valuable when it can move into tools people already use. The pipeline supports a range of output destinations.
Google Sheets
Send structured outputs into spreadsheet workflows.
Excel
Export results into familiar formats for analysis and reporting.
Google Drive & OneDrive
Store processed outputs in cloud storage platforms.
Webhooks
Send extracted data to external systems and automation pipelines.
Deliver processed results through notification or distribution workflows.
And more
Slack, Teams, and additional output destinations via the webhook integration layer.
Try the System
Run a document through the pipeline
Run documents through the pipeline to see how extraction and structured output works in practice. This experience is designed as a technical showcase.
Try with sample documents to explore supported extraction scenarios.
Experimental system · built for research, testing, and demonstration.
Source Code
Source code and system design
DocPipeline is also a technical exploration of document extraction architecture, including OCR abstraction, extraction logic, output shaping, and integration workflows.
DocPipeline on GitHub
Explore implementation details and architecture decisions behind the system.
OCR Pipeline
Pluggable OCR abstraction supporting PaddleOCR and Azure Document Intelligence. Swap engines without changing extraction logic.
Extraction Logic
LLM-backed document classification and field mapping. Each document type has a schema definition that maps OCR output to typed, named fields.
Pipeline Architecture
Queue-based async processing with background workers. Supports single-file and bulk ingestion. Auto-export rules fire on job completion.
See structured document extraction in practice
Run a document through the pipeline, review the architecture, and follow the evolution of the system across OCR, AI extraction, and workflow integration.
Experimental system built for research, testing, and demonstration.