Intelligent Document Processing (IDP) sits at the intersection of OCR, computer vision, NLP, and document AI — converting unstructured documents (PDFs, scanned images, forms, contracts) into structured, machine-readable data. The field matters because the vast majority of enterprise data is locked in unstructured document formats, creating bottlenecks in finance, healthcare, legal, and logistics workflows. The critical insight practitioners learn quickly is that document processing is a pipeline problem: OCR quality, preprocessing, layout detection, chunking, and validation each compound — a weakness at any stage degrades the entire system, and no single model solves the whole problem.
What This Cheat Sheet Covers
This topic spans 14 focused tables and 87 indexed concepts. Below is a complete table-by-table outline of this topic, spanning foundational concepts through advanced details.
Table 1: IDP Pipeline Stages
The four core phases every intelligent document processing system must traverse — from raw document ingestion to structured data delivery. Each stage has its own tooling and failure modes; understanding the sequence helps diagnose where a pipeline underperforms and prevents misattributing extraction errors to the wrong component.
| Stage | Example | Description |
|---|---|---|
files = glob.glob("inbox/*.pdf") | Entry point: collect documents from email, S3, FTP, scanner, or API; normalize file formats (PDF, TIFF, DOCX) for downstream processing. | |
doc_type = classifier.predict(page_image) | Clean and classify incoming pages: apply OCR preprocessing (binarization, deskewing), detect document type (invoice vs. contract vs. ID) before extraction begins. |