Parse Documents Including Images, Tables, Equations, Charts, and Code.

Author(s): Ahmed Boulahia

Originally published on Towards AI.

Enhance Your RAG Pipeline by Using SmolDocling to Parse Complex Documents (Tables, Equations, Charts & Code) into Your Vector DBImage created by the authorVision + Structure: SmolDocling is a new 256M-parameter model that reads entire document pages and converts them into a rich DocTags markup format capturing content and layout.Compact & Fast: Despite its small size, it matches the accuracy of models 10–27× larger. It runs quickly (≈0.35s/page on an A100 GPU).Key Features: Built-in OCR with bounding boxes, formula/code recognition, table/chart parsing, list grouping, caption linking, etc., all in one end-to-end package.

Have you ever tried to copy-paste text from a PDF research paper and ended up with gibberish, missing figures, or malformed equations? Complex documents are often packed with non-text elements like images, graphs, tables and math , that simple text-based AI can’t handle.

SmolDocling aims to change that, it’s a multimodal AI model designed to process a whole page image and output a single, structured representation of everything on it.

In this post, we’ll see why combining vision and language is essential for modern document AI, and how SmolDocling’s features set let it convert complex docs end-to-end.

Traditional document AI often treated pages as “just text”. One common pattern was: run an OCR engine to get all the words (and their positions), then feed that into a text model.

Systems like LayoutLM… Read the full blog for free on Medium.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI