How to use PaddleOCR? Turn PDFs and images into structured data usable by AI

Sat, 06 Jun 2026 22:26:00 +0800

PaddlePaddle/PaddleOCR is a mature OCR and document parsing tool. The project description is already very close to the AI scenario: turning PDF or image documents into structured data, connecting images/PDFs with LLM, and supporting 100+ languages.

If you do RAG, knowledge base, bill recognition, PDF parsing or scanned document processing, OCR is an unavoidable prerequisite.

What it can do

PaddleOCR is suitable for:

Image text recognition;
PDF document parsing;
Table and layout structure extraction;
Multilingual OCR;
Convert documents to Markdown;
Data cleaning before RAG;
Processing of documents such as bills, certificates, contracts, papers, etc.

It is not simply “recognizing a few lines of text”, but is increasingly biased toward understanding links to complete documents.

Why is it important for LLM?

LLM itself is not good at handling complex scans directly. Even if multimodal models can read images, batch document processing still requires a stable, traceable, and structured OCR pipeline.

Tools like PaddleOCR can first turn the document into:

text;
coordinates;
forms;
paragraph;
Layout structure;
Markdown or structured JSON.

Then it is handed over to LLM for summary, question and answer, extraction and verification.

What should you pay attention to when using it?

The quality of OCR is very input dependent:

Scan clarity;
Tilt and noise;
Table complexity;
handwriting;
Multi-column typesetting;
Professional terminology;
Language mixing.

In a production system, don’t just look at the recognition rate, but also look at post-processing, manual verification, and error traceability.

Summary

PaddleOCR is a key tool in the AI document processing link. Chinese and multi-language document scenarios are particularly worthy of attention.

If you want to do PDF RAG, document knowledge base or scanned document automation, do the OCR and layout parsing first, which is more stable than throwing the image directly to the model.

Reference sources