Publication

ICDAR 2025 Competition on Historical Map Text Detection, Recognition, and Linking

2026

Abstract

Historical maps are valuable for history, social sciences, and linguistics but pose challenges for automatic transcription. This competition edition continues to address detection, recognition, and linking of text in historical maps, with new features: expanded French Land Registers data, a new Taiwanese dataset with Chinese characters, synthetic training data, and improved linking evaluation metrics. Seven teams participated with over 25 submissions across four tasks and three datasets. While detection performance is strong, recognition and linking remain difficult, though improvements were seen with Bézier curve line fitting and enhanced linking pipelines. All resources are publicly available on Zenodo (https://zenodo.org/communities/icdar-maptext).

Official source

https://infoscience.epfl.ch/entities/publication/a52cd0da-e2b7-45fd-9338-0ec41d74a6bc

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Graph Chat

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

2026

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/a52cd0da-e2b7-45fd-9338-0ec41d74a6bc

About this result

Related concepts (24)

Natural language generation

Natural language generation (NLG) is a software process that produces natural language output. A widely-cited survey of NLG methods describes NLG as "the subfield of artificial intelligence and computational linguistics that is concerned with the construction of computer systems than can produce understandable texts in English or other human languages from some underlying non-linguistic representation of information". While it is widely agreed that the output of any NLG process is text, there is some disagreement about whether the inputs of an NLG system need to be non-linguistic.

Optical character recognition

Optical character recognition or optical character reader (OCR) is the electronic or mechanical conversion of s of typed, handwritten or printed text into machine-encoded text, whether from a scanned document, a photo of a document, a scene photo (for example the text on signs and billboards in a landscape photo) or from subtitle text superimposed on an image (for example: from a television broadcast).

Document processing

Document processing is a field of research and a set of production processes aimed at making an analog document digital. Document processing does not simply aim to photograph or a document to obtain a , but also to make it digitally intelligible. This includes extracting the structure of the document or the layout and then the content, which can take the form of text or images. The process can involve traditional computer vision algorithms, convolutional neural networks or manual labor.

Related publications (1)

ICDAR 2025 Competition on Historical Map Text Detection, Recognition, and Linking

Chat with Graph Search

Text Detection and Recognition in Images and Videos