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DRR 2012 : Document Recognition and Retrieval XIX


Conference Series : Document Recognition and Retrieval
When Jan 22, 2012 - Jan 26, 2012
Where San Francisco
Submission Deadline Jul 11, 2011
Notification Due Sep 12, 2011
Final Version Due Nov 14, 2011
Categories    OCR

Call For Papers

We are pleased to announce the 19th Document Recognition and Retrieval Conference (DRR), to be held on 22-26 January 2012, in San Francisco, CA, USA. DRR is an international conference focused on state-of-the-art research in document recognition and retrieval, for offline, online and web documents. The conference is part of the Electronic Imaging Symposium, which brings together researchers from various backgrounds related to electronic imaging for an exciting research event. The conference will include oral/poster presentations, invited talks and invited papers. Accepted papers will be published in DRR Proceedings. For the sixth year, the Best Student Paper will be selected among papers whose first authors are full-time students. Note that after many years in San Jose, the conference has been held very succesfully in San Francisco in 2011.

We are soliciting papers describing algorithms and systems in all aspects of document recognition and retrieval, for offline, online and Web documents. Specifically, we are encouraging papers enlarging the frontiers and dealing for instance with multi-modal information such as online and offline, or speech and handwriting, etc.

Recognizing handwritten or degraded machine printed documents (e.g. faxed or historical documents) remains a challenging problem. Document recognition and understanding are concerned with fully reconstructing a document in electronic form consistent with the original format (fonts, layout etc.) and capturing the underlying logical structure (e.g. section headings, region types, references, and reading order) of the document. Among the challenges for machine-printed documents are complex layouts (text written on images, complex backgrounds, etc.), degraded and noisy documents, and robust recognition of tables and equations. Handwritten documents with unconstrained writing style pose additional challenges due to increased variability and segmentation ambiguities. Non-textual elements in documents form another class of interesting problems. These include the extraction and recognition of logos and signatures, and the conversion of line drawings in documents from raster to vector format. Web documents pose both similar and new challenges.

One of the primary reasons for digitizing existing paper materials is to simplify retrieval and organization of information. In this regard we are particularly interested in papers which address any of the following issues: retrieval in the presence of noise; retrieval based on sketches, images, tables, diagrams or other non-linguistic objects that appear in the document; retrieval based on text appearing with non-standard alignment, in images or graphics; recognition and tagging of mathematical arrays and equations which serve as indicators of subject content or methodology used in the document; novel methods for retrieval and organization of information based on text or other information in a document. Papers addressing retrieval-specific issues are encouraged to use standard performance metrics such as ROC and precision-recall curves.

Papers are solicited in, but not limited to, the following areas:

Document Recognition

Text recognition: machine-printed, handwritten documents; paper, tablet, camera, video sources

Writer/style identification, verification, adaptation

Graphics recognition: vectorization (e.g. for line-art, maps and technical drawings), signature, logo and graphical symbol recognition, figure, chart and graph recognition, diagrammatic notations (e.g. music, mathematical notation)

Document layout analysis and understanding: document and page region segmentation, form and table recognition, document understanding through combined modalities (e.g. speech and images)

Evaluation: performance metrics, document degradation models

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, web page analysis (including wikis and blogs)

Document Retrieval

Indexing and Summarization : (noisy) text documents (messages, blogs, etc.), imaged documents, entity tagging from OCR'ed text, 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'ed text, retrieval of noisy text documents (messages, blogs, etc.), cross and multi- lingual retrieval

Evaluation: relevance and performance metrics, evaluation protocols, benchmarking

Additional topics: relevance feedback, impact of recognition accuracy on retrieval performance, and digital libraries including systems engineering and quality assurance

Related Resources

DocEng 2020   The 20th ACM Symposium on Document Engineering
SDP 2020   1st Workshop on Scholarly Document Processing and Shared Tasks (SDP 2020) @ EMNLP 2020
KDIR 2020   12th International Conference on Knowledge Discovery and Information Retrieval
CBIR 2020   Content-Based Image Retrieval: where have we been, and where are we going
CCVPR 2020   2020 3rd International Joint Conference on Computer Vision and Pattern Recognition (CCVPR 2020)
ACM--NLPIR--Ei Compendex and Scopus 2020   ACM--2020 4th International Conference on Natural Language Processing and Information Retrieval (NLPIR 2020)--Scopus, Ei Compendex
SPRA--EI Compendex, Scopus 2020   2020 Symposium on Pattern Recognition and Applications (SPRA 2020)--EI Compendex, Scopus
SIPPR 2021   2021 International Symposium on Signal, Image Processing and Pattern Recognition (SIPPR 2021)
ICTIR 2020   International Conference on the Theory of Information Retrieval