International Journal on Document Analysis and Recognition (IJDAR) is an academic journal published by Springer. (ISSN 1433-2833, impact factor 2.5, CCF C).
Aims and 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.
This journal publishes articles of four primary types - original research papers, correspondence, overviews and summaries, and research notes. Special issues on active areas of research are encouraged. We welcome submissions in all areas related to document analysis and recognition.
New datasets to support document analysis research are generally of interest to the readership of IJDAR. Several conditions need to be satisfied first, however, before a paper presenting such a dataset will be considered for publication in the journal. (a) The dataset must be new, (b) The dataset must be freely available to the internationa l research community on a public repository, and (c) The dataset and its distribution must follow all applicable laws and regulations regarding personal data privacy and usage rights. In addition, a new dataset in-and-of-itself does not make a publishable paper. The paper must include, in addition, experimental results that illustrate and demonstrate the utility and significance of the new dataset.
Possible topics include:
- Document Image Processing
- Document Models
- Handwriting Models and Analysis
- Character and Word Recognition
- On-line Recognition
- Pen Based Computing
- Multi-lingual Processing
- Physical and Logical Analysis
- Graphics Recognition
- Map and Line Drawing Understanding
- Storage and Retrieval of Documents
- Text Analysis and Processing
- Natural Language Issues
- Information Extraction and Filtering
- Performance Evaluation
- Document Authentification and Validation
- Implementations, Applications and Systems
as well as non-traditional topics such as:
- Processing Text in Other Contexts
- Multimedia and Hypermedia Analysis
- Time Varying Documents
- Distributed Document Collections (Digital Libraries).