RISE Crash Course: "Information Extraction from Images with AI"

Slides for RISE Crash Course: "Information Extraction from Images with AI" In this two-hour course, you will learn how multimodal large language models, such as ChatGPT-4o, Gemini 1.5, or Claude Sonnet 3.5, can be used to extract structured information directly from images. This approach eliminates the often necessary intermediate step of text recognition and transcription that is common in traditional methods (such as Transkribus). Using concrete examples from ongoing research projects, the course will demonstrate the practical possibilities and limitations of this technology. It will also address the technical and methodological prerequisites required for successful implementation. Additionally, aspects of data quality, the FAIRness (Findability, Accessibility, Interoperability, Reusability) of the extracted data, as well as the associated costs, will be considered and reflected upon.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-10-22
DOI
https://doi.org/10.5281/zenodo.17416426
Primary Topic
Handwritten Text Recognition Techniques
Type
article
Field-Weighted Citation Impact
0.00
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article

RISE Crash Course: "Information Extraction from Images with AI"

Zenodo (CERN European Organization for Nuclear Research)
Handwritten Text Recognition Techniques
article

RISE Crash Course: "Information Extraction from Images with AI"

article en

Abstract

Slides for RISE Crash Course: "Information Extraction from Images with AI" In this two-hour course, you will learn how multimodal large language models, such as ChatGPT-4o, Gemini 1.5, or Claude Sonnet 3.5, can be used to extract structured information directly from images. This approach eliminates the often necessary intermediate step of text recognition and transcription that is common in traditional methods (such as Transkribus). Using concrete examples from ongoing research projects, the course will demonstrate the practical possibilities and limitations of this technology. It will also address the technical and methodological prerequisites required for successful implementation. Additionally, aspects of data quality, the FAIRness (Findability, Accessibility, Interoperability, Reusability) of the extracted data, as well as the associated costs, will be considered and reflected upon.

Zenodo (CERN European Organization for Nuclear Research)
University of Basel (CH)
Openalex Percentile: Top 43%
Handwritten Text Recognition Techniques
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