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International Journal on Document Analysis and Recognition (IJDAR)

International Journal on Document Analysis and Recognition (IJDAR) OnlineFirst articles

14-06-2021 | Special Issue Paper

Data Augmentation using Geometric, Frequency, and Beta Modeling approaches for Improving Multi-lingual Online Handwriting Recognition

The lack of large training data in the context of deep learning applications is a serious issue investigated by many studies that deal with the current challenge. In this paper, we introduce new data augmentation methods that generate more shape …

11-06-2021 | Special Issue Paper

Learning from similarity and information extraction from structured documents

The automation of document processing has recently gained attention owing to its great potential to reduce manual work. Any improvement in information extraction systems or reduction in their error rates aids companies working with business …

11-06-2021 | Special Issue Paper

A two-step framework for text line segmentation in historical Arabic and Latin document images

One of the most important preliminary tasks in a transcription system of historical document images is text line segmentation. Nevertheless, this task remains complex due to the idiosyncrasies of ancient document images. In this article, we …

10-06-2021 | Original Paper

SKFont: skeleton-driven Korean font generator with conditional deep adversarial networks

In our research, we study the problem of font synthesis using an end-to-end conditional deep adversarial network with a small sample of Korean characters (Hangul). Hangul comprises of 11,172 characters and is composed by writing in multiple …

08-06-2021 | Special Issue Paper

Self-supervised deep metric learning for ancient papyrus fragments retrieval

This work focuses on document fragments association using deep metric learning methods. More precisely, we are interested in ancient papyri fragments that need to be reconstructed prior to their analysis by papyrologists. This is a challenging …

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About this journal

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.

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