![Building Local OCR Application SmolDocling: A Step-by-Step Guide [Part 2] Building Local OCR Application SmolDocling: A Step-by-Step Guide [Part 2]](https://miro.medium.com/v2/resize:fit:500/1*7BC6wHBRhlZ_skdn89WvIg.png)
Building Local OCR Application SmolDocling: A Step-by-Step Guide [Part 2]
Last Updated on April 15, 2025 by Editorial Team
Author(s): Youssef Hosni
Originally published on Towards AI.
If you want to build a Local OCR application so that you do not need to send your documents to APIs. You can use ππ¦π¨π₯ππ¨ππ₯π’π§π , which is an ultra-compact vision-language model for end-to-end multi-modal document conversion that can be used for LocalOCR.
π¦πΊπΌπΉππΌπ°πΉπΆπ»π΄ 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.
Itβs 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 27Γ larger in full-page transcription, layout detection, and code recognition.
In this two-part hands-on tutorial, weβll build a local OCR application using π¦πΊπΌπΉππΌπ°πΉπΆπ»π΄. In the second part, weβll develop the OCR web application, and weβll integrate everything from the first part and create the application interface using streamlit.
Set up the Working EnvironmentBuild the OCR Pipeline with SmolDoclingBuild the Streamlit ApplicationRun the OCR Application
You can find the codes and data used in this article in this GitHub Repo.
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Published via Towards AI