PaddleOCR vs Surya

PaddleOCR is an open-source OCR and document-processing toolkit for extracting text and structure from documents. Its PP-StructureV3 component turns complex PDFs and document images into Markdown and JSON while preserving their organization, and supports table extraction. PaddleOCR-VL handles multilingual document parsing across 109 languages, while PP-OCRv6 supports 50 languages in one model. PaddleOCR 3.0 also recognizes handwriting. For large document collections, PP-ChatOCRv4 extracts key information and integrates ERNIE 4.5. Models can be used online through the official website or called through an API; the official free API allows up to 20,000 pages of document parsing per day. Documentation describes local, C++, service, Android, iOS, and browser deployment options, among others. The license permits use, modification, distribution, and sale without restriction. SDK languages listed are Python, TypeScript, and Go. Support is available through GitHub Issues. One compatibility note: code written for PaddleOCR 2.x may not run with version 3.x.

Surya is a 650M-parameter OCR model for extracting text and analyzing document structure. It handles layout, reading order and tables, and supports OCR in 91 languages. Surya 2 returns page content as structured blocks that can include HTML, polygons, bounding boxes, confidence values and reading order. Table recognition can provide row and column geometry; full mode can also return HTML with spanning cells and header rows. Equations found in page OCR are represented as KaTeX-compatible LaTeX within math tags. Users can work with a Python package, command-line tools or an interactive Streamlit app for images and PDFs. Layout, OCR and table recognition require an inference backend: vLLM on NVIDIA GPUs or llama.cpp on CPU and Apple Silicon, or a connection to an existing server. Surya is free and open source. The code uses Apache 2.0, while the model weights have a separate modified AI Pubs Open Rail-M license with commercial-use conditions. It is specialized for documents, not photos or natural scenes.