Conference Information

ICDAR 2026: International Conference on Document Analysis and Recognition

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ICDAR
Submission Date:
2026-02-27
Notification Date:
2026-05-15
Conference Date:
2026-08-30
Location:
Vienna, Austria
Years:
20
CCF: C   ICORE: A   QUALIS: A2   Viewed: 138571   Tracked: 81   Attend: 20

Call For Papers

ICDAR 2026 (International Conference on Document Analysis and Recognition) is a CCF C / ICORE A / QUALIS A2 conference held in Vienna, Austria on 2026-08-30. The paper submission deadline is 2026-02-27. Acceptance notifications are sent on 2026-05-15.

The International Conference on Document Analysis and Recognition (ICDAR) is the premier scientific venue in the field of document analysis and recognition. This conference brings together international experts from academia and industry to present their latest research results, share their experiences and to promote research and development in all aspects of recognition and analysis of documents, with a particular focus on document image analysis. The 20th edition of this flagship conference will be held as an in person event in Vienna, Austria, from August 30 to September 04, 2026. Topics of interest to the conference include, but are not limited to: Document image processing Physical and logical layout analysis Text and symbol recognition Handwriting recognition Document analysis systems Document classification Indexing and retrieval of documents Document synthesis Extracting document semantics NLP for document image analysis Office automation Graphics recognition Human document interaction Document Representation Modeling Private and secure document understanding Structured document generation Multimedia document analysis Mobile text recognition Pen-based document analysis Scene text detection and recognition Recognition of tables and formulas Historical document analysis Signature verification Document summarization and translation Document forensics and provenance Medical document analysis Document analysis for literature search Gold-standard benchmarks and datasets Foundation models for document understanding
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