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

Authors: Hindermann, Maximilian;

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

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.

Related Organizations
Keywords

LLM, GPT, image, SSH, data extraction, MLLM

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    influence
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green