But what happens when the valuable information is hidden in scanned documents or images? This is where Optical Character Recognition (OCR) comes into play. Choosing the right OCR tool is a crucial first step in building the RAG database. An inaccurate OCR process will result in incorrect chunks, leading to poorer retrieval quality and inaccurate responses from the chatbot.
In this article, we look at two leading open source solutions: Tesseract OCR and PaddleOCR in the context of data acquisition for RAG.
What is RAG and the role of OCR?
A RAG chatbot usually works in three phases:
- Data acquisition and indexing: documents (often PDFs, images) are processed. OCR converts visual text into machine-readable text. This text is broken down into chunks, embedded and stored in a vector memory.
- Retrieval: In response to a user query, the system searches for the most relevant text chunks in the database.
- Generation: The LLM receives the user query and the retrieved chunks in order to generate an informed response.
The quality of the RAG system depends directly on the quality of the extracted data.
Tesseract OCR: The classic
Tesseract is the oldest and best-known open source OCR engine, originally developed by Hewlett-Packard and now maintained by Google.
Advantages for RAG
- Broad language support: Supports over 100 languages, which is an advantage for multilingual RAG databases.
- Stability and experience: Long established with a large community and many available interfaces (e.g. pytesseract in Python).
- Lightweight: Works well on CPUs, which reduces hardware requirements for data preprocessing and is suitable for cost-effective solutions.👎 Disadvantages in the RAG contextDependence on image quality: Tesseract often requires intensive preprocessing of images to ensure accuracy (denoising, scaling).
Weaknesses with complex layouts: Historically, Tesseract has had difficulties with tables, handwritten text or heavily distorted images - exactly the types of documents often found in corporate archives.
PaddleOCR:
The deep learning specialistPaddleOCR is an advanced open source toolkit based on Baidu's PaddlePaddle deep learning framework. It separates text detection and recognition, resulting in high accuracy.
Benefits for RAG - High accuracy:
- Provides state-of-the-art performance, especially on complex documents and "scene text" (text in natural images). Some benchmarks consider it more accurate than Tesseract, especially for non-English or complex fonts.
- Structure recognition: Newer versions (such as PaddleOCR 3.0) offer advanced features such as PP-StructureV3 to intelligently convert complex documents (including tables and formulas) into structured formats such as Markdown or JSON. This is worth its weight in gold for RAG as the structural integrity of the source material is preserved.
- Lightweight models: Provides optimized lightweight models for real-time applications or resource-constrained environments.
Disadvantages in the RAG context
- Installation complexity: Based on the PaddlePaddle framework, which can be less common than PyTorch or TensorFlow, which can mean an additional learning curve.
- Hardware requirements: GPU resources are recommended for maximum precision, which can drive up the cost of indexing.
Conclusion and recommendation for RAG data acquisition
The decision between Tesseract and PaddleOCR depends heavily on the type of data you want to integrate into your RAG database
Tesseract OCR
- Accuracy: Good, but highly dependent on pre-processing
- Structure handling: Moderate, requires additional tools
- Language support: Very broad (>100 languages)
- Hardware: CPU-based, cost-efficient
PaddleOCR
- Accuracy: Very good, deep learning-based
- Structure handling: Excellent (PP-StructureV3)
- Language support: Very good (>80 languages)
- Hardware: GPU-optimized for best performance
For a future-proof and high-performance RAG database, PaddleOCR is often the better choice, especially due to its superior ability to recognize structured text (such as tables and complex layouts) and convert it into machine-readable formats (Markdown).
Preserving this structure is crucial for chunking and embedding, as context and relationships between sections of text are better preserved. However, if you need to process an extremely large variety of rare languages in simple documents and no GPU is available, Tesseract remains a solid, cost-effective option.
For the best performance, developers should ideally consider a combination: PaddleOCR for accurate extraction of structured documents and possibly a fallback strategy or specialized module for handwritten or extremely corrupted documents.