Call for Papers

Conference overview

Document Recognition and Retrieval (DRR) is one of the leading international conferences devoted to current research in document analysis, recognition, and retrieval. The Conference Chairs and Program Committee invite all researchers working on document recognition and retrieval to submit original research papers. Papers are presented in oral and poster sessions at the conference, along with invited talks by leading researchers.

Accepted papers are published by IS&T in the conference proceedings. Papers will also be indexed by DBLP. At the conference a Best Student Paper Award is presented. Papers are solicited in, but not limited to, the areas below.

Submission Requirements

Papers are expected to be within the 8-12 page range.

The review process takes into account both the quality and the depth of development of the scientific and experimental content of a paper. Papers should clearly identify the problem addressed in the work, identify the original contribution(s) of the paper, relate the paper to previous work, justify and document the methods of the paper, and provide substantive experimental and/or theoretical evaluation. The program committee favors papers that take advantage of the opportunity to develop, argue, and provide compelling experimental evidence. We recommend using the LaTeX template for preparing submissions. Note that it is the author’s responsibility to produce the PDF for upload in the submission process, regardless of the original source format. For accepted submissions, the final published manuscripts are expected to be a minimum of 8 pages.

Questions concerning the conference may be addressed to: drr2016(at)

Conference topics (including, but not limited to)

  1. Document recognition

    • Text recognition: machine-printed documents, handwritten documents; paper, tablet, camera, and video sources
    • Writer/style identification, verification, and adaptation
    • Graphics recognition: vectorization (e.g. for line art, maps, and technical drawings), signature, logo and graphical symbol recognition, figure, chart and graph recognition, and diagrammatic notations (e.g. music, mathematical notation)
    • Document layout analysis and understanding: document and page region segmentation, form and table recognition, and document understanding through combined modalities (e.g. speech and images)
    • Evaluation: performance metrics, document degradation models, data collection, benchmarking, ground-truthing
    • Additional topics: document image filtering, enhancement and compression, document clustering and classification, machine learning (e.g. integration and optimization of recognition modules), historical and degraded document images (e.g. fax), multilingual document recognition, and web page analysis (including wikis and blogs)
  2. Document retrieval

    • Indexing and summarization: text documents (messages, blogs, etc.), imaged documents, entity tagging from OCR output, and text categorization
    • Query languages and modalities: content-based image retrieval (CBIR) for documents, keyword spotting, non-textual query-by-example (e.g. tables, figures, math), querying by document geometry and/or logical structure,
    • approximate string matching algorithms for OCR output, retrieval of noisy text documents (messages, blogs, etc.), cross and multi-lingual retrieval
    • Discovery and browsing: pattern discovery, trend mining, topic modeling and analysis, clustering
    • Evaluation: relevance and performance metrics, evaluation protocols, and benchmarking
    • Additional topics: relevance feedback, impact of recognition accuracy on retrieval performance, and digital libraries including systems engineering and quality assurance