Sponsored by the International Association for Pattern Recognition, this journal is focused on publishing articles that cover all areas related to document analysis and recognition. This includes contributions dealing with computer recognition of characters, symbols, text, lines, graphics, images, handwriting, signatures, as well as automatic analyses of the overall physical and logical structures of documents, with the ultimate objective of a high-level understanding of their semantic content.
The International Journal on Document Analysis and Recognition (IJDAR) publishes articles of four primary types: original research papers, correspondence, overviews and summaries, and system descriptions. It also features special issues on active areas of research.
Currently indexed in:
Academic Search Alumni Edition, Academic Search Complete, Academic Search Premier, Bibliography of Linguistic Literature, Compendex, Compuscience, Computer Science Index, Current Abstracts, Current Contents/Engineering, Computing, and Technology, DBLP, Google, INSPEC, Journal Citation Reports/Science Edition, OCLC ArticleFirst Database, OCLC FirstSearch Electronic Collections Online, PASCAL, SCOPUS, Science Citation Index Expanded, Summon by Serial Solutions, TOC Premier.
- Sponsored by the International Association for Pattern Recognition.
- Covers all areas related to document analysis and recognition.
- Includes contributions dealing with computer recognition of characters, symbols, text, lines, graphics, images, handwriting, and signatures.
- Examines automatic analyses of the overall physical and logical structures of documents.
- Koichi Kise,
- Daniel Lopresti,
- Simone Marinai
- Publishing model
- Hybrid (Transformative Journal). Learn about publishing Open Access with us
- 1.486 (2019)
- Impact factor
- 1.514 (2019)
- Five year impact factor
- 70 days
- Submission to first decision
- 357 days
- Submission to acceptance
- 35,247 (2019)
Translating math formula images to LaTeX sequences using deep neural networks with sequence-level training
A robust watermarking approach for security issue of binary documents using fully convolutional networks
Authors (first, second and last of 4)
About this journal
- Electronic ISSN
- Print ISSN
- Abstracted and indexed in
- ACM Digital Library
- Current Contents/Engineering, Computing and Technology
- EBSCO Academic Search
- EBSCO Applied Science & Technology Source
- EBSCO Computer Science Index
- EBSCO Computers & Applied Sciences Complete
- EBSCO Discovery Service
- EBSCO Engineering Source
- EBSCO STM Source
- EI Compendex
- Google Scholar
- Institute of Scientific and Technical Information of China
- Japanese Science and Technology Agency (JST)
- Journal Citation Reports/Science Edition
- OCLC WorldCat Discovery Service
- ProQuest Advanced Technologies & Aerospace Database
- ProQuest Central
- ProQuest SciTech Premium Collection
- ProQuest Technology Collection
- ProQuest-ExLibris Primo
- ProQuest-ExLibris Summon
- Science Citation Index Expanded (SciSearch)
- TD Net Discovery Service
- UGC-CARE List (India)
- WTI Frankfurt eG