Automatic Text Recognition

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.

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.

This free online course provides a practical introduction to working with digital texts and applying some of the tools available to digitise and interrogate them. It will help you to decide whether to use digital approaches to textual analysis and to select the correct tools for your research project.

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).

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.

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

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

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

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