Building Local OCR Application SmolDocling: A Step-by-Step Guide [Part 1]

Author(s): Youssef Hosni

Originally published on Towards AI.

If you want to build a Local OCR application such that you do not need to send your documents to APIs. You can use 𝐒𝐦𝐨𝐥𝐃𝐨𝐜𝐥𝐢𝐧𝐠, which is an ultra-compact vision-language model for end-to-end multi-modal document conversion that can be used for LocalOCR.

𝗦𝗺𝗼𝗹𝗗𝗼𝗰𝗹𝗶𝗻𝗴 is a compact 256M open-source vision language model designed for OCR. It offers end-to-end document conversion without complex pipelines, allowing a single small model to handle everything. It’s fast and efficient, processing a page in just 0.35 seconds on a consumer GPU with less than 500MB VRAM. Despite its small size, it delivers high accuracy, outperforming models 27× larger in full-page transcription, layout detection, and code recognition.

In this two-part hands-on tutorial, we’ll build a local OCR application using 𝗦𝗺𝗼𝗹𝗗𝗼𝗰𝗹𝗶𝗻𝗴. In the first part, we’ll develop the OCR pipeline step by step, breaking down each component. In the second part, we’ll integrate everything and create the application interface.

Introduction to OCR & SmolDocling ModelSetting Up the Working EnvironmentInitializing the Processor and ModelPreparing OCR PromptGenerating OCR OutputWrapping the OCR Pipeline

You can find the codes and data used in this article in this GitHub Repo.

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Published via Towards AI