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Found 9 papers, 4 papers with code
Yes but.. Can ChatGPT Identify Entities in Historical Documents?
Large language models (LLMs) have been leveraged for several years now, obtaining state-of-the-art performance in recognizing entities from modern documents.
DocILE Benchmark for Document Information Localization and Extraction
This paper introduces the DocILE benchmark with the largest dataset of business documents for the tasks of Key Information Localization and Extraction and Line Item Recognition.
DocILE 2023 Teaser: Document Information Localization and Extraction
The DocILE 2023 competition, hosted as a lab at the CLEF 2023 conference and as an ICDAR 2023 competition, will run the first major benchmark for the tasks of Key Information Localization and Extraction (KILE) and Line Item Recognition (LIR) from business documents.
Named Entity Recognition and Classification on Historical Documents: A Survey
After decades of massive digitisation, an unprecedented amount of historical documents is available in digital format, along with their machine-readable texts.
Alleviating Digitization Errors in Named Entity Recognition for Historical Documents
This paper tackles the task of named entity recognition (NER) applied to digitized historical texts obtained from processing digital images of newspapers using optical character recognition (OCR) techniques.
