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Explore this curriculum on Automatic Text Recognition (ATR) and learn how to efficiently extract full text from heritage material images. Perfectly tailored for researchers, librarians, and archivists, these resources not only enhance your archival research and preservation efforts but also unlock the potential for computational analysis of your sources.

Increase the visibility of your ATR output while fostering Open Science.

Discover the subtleties of region and line segmentation and learn about the purpose of layout analysis for Automatic Text Recognition!

Dive into the fine-tuning of Automatic Text Recognition outputs!

This tutorial explores where and how to find, create, and collect images of textual material, a crucial initial step in any process using Automatic Text Recognition (ATR).

Get ready to improve the quality of your scanned images before moving to the processing phase of your ATR project.

Tools for machine transcription of handwriting are practical and labour-saving if you need to analyse or present text in digital form. This lesson will explain how to write a Python program to transcribe handwritten documents using Microsoft's Azure Cognitive Services, a commercially available service that has a cost-free option for low volumes of use.

Kick off your journey into Automatic Text Recognition (ATR) with our introductory tutorial video. This is the first video of a tutorial series dedicated to extracting full text from scanned images.