International Journal on Document Analysis and Recognition

Aims & Scope

The large number of existing documents and the production of a multitude of new ones every year raise important issues in efficient handling, retrieval and storage of these documents and the information which they contain.

This has led to the emergence of new research domains dealing with the recognition by computers of the constituent elements of documents - including characters, symbols, text, lines, graphics, images, handwriting, signatures, etc.

In addition, these new domains deal with automatic analyses of the overall physical and logical structures of documents, with the ultimate objective of a high-level understanding of their semantic content.

We have also seen renewed interest in optical character recognition (OCR) and handwriting recognition during the last decade.

Document analysis and recognition are obviously the next stage.

Automatic, intelligent processing of documents is at the intersections of many fields of research, especially of computer vision, image analysis, pattern recognition and artificial intelligence, as well as studies on reading, handwriting and linguistics.

Although quality document related publications continue to appear in journals dedicated to these domains, the community will benefit from having this journal as a focal point for archival literature dedicated to document analysis and recognition.

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	2.5
2024	1.80

SJR (SCImago Journal Rank)

Year	Value
2024	0.830

Quartile

Year	Value
2024	Q1

h-index

Year	Value
2024	61

Journal Rank

Year	Value
2024	6432

Journal Citation Indicator

Year	Value
2024	322

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Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar
• Web of Science

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Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Computer Science, designed to support cutting-edge academic discovery.

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Licensing & Copyright

This journal operates under an Open Access model. Articles are freely accessible to the public immediately upon publication. The content is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), allowing users to share and adapt the work with proper attribution.

Copyright remains with the author(s), and no permission is required for non-commercial use, provided the original source is cited.

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Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• The IAM-database: an English sentence database for offline handwriting recognition

  Citation: 1082

  Authors: U.-V., H.

• Camera-based analysis of text and documents: a survey

  Citation: 305

  Authors: Jian, David, Huiping

• Word spotting for historical documents

  Citation: 297

  Authors: Tony M., R.

• Text line segmentation of historical documents: a survey

  Citation: 287

  Authors: Laurence, Abderrazak, Bruno

• Object count/area graphs for the evaluation of object detection and segmentation algorithms

  Citation: 275

  Authors: Christian, Jean-Michel

• Mathematical expression recognition: a survey

  Citation: 235

  Authors: Kam-Fai, Dit-Yan

• ICDAR 2003 robust reading competitions: entries, results, and future directions

  Citation: 216

  Authors: Simon M., Alex, Luis, Anthony, Shirley, Robert, Kazuki, Hiroki, Masayuki, Hiroaki, Hidetoshi, JunMin, WuWen, Christian, Jean-Michel, Leon, Marcel, Xiaofan

• Recognition and retrieval of mathematical expressions

  Citation: 197

  Authors: Richard, Dorothea

• Online handwriting recognition: the NPen++ recognizer

  Citation: 187

  Authors: S., S., J., A.

• Document image binarization using background estimation and stroke edges

  Citation: 184

  Authors: Shijian, Bolan, Chew Lim

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