Twenty Years of Document Image Analysis in PAMI
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Database for Handwritten Text Recognition Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Social Mechanism of Reputation Management in Electronic Communities
CIA '00 Proceedings of the 4th International Workshop on Cooperative Information Agents IV, The Future of Information Agents in Cyberspace
Review on Computational Trust and Reputation Models
Artificial Intelligence Review
A stroke regeneration method for cleaning rule-lines in handwritten document images
Proceedings of the International Workshop on Multilingual OCR
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
Results of the RIMES Evaluation Campaign for Handwritten Mail Processing
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
IBN SINA: a database for research on processing and understanding of Arabic manuscripts images
DAS '10 Proceedings of the 9th IAPR International Workshop on Document Analysis Systems
A platform for storing, visualizing, and interpreting collections of noisy documents
AND '10 Proceedings of the fourth workshop on Analytics for noisy unstructured text data
Table Detection in Noisy Off-line Handwritten Documents
ICDAR '11 Proceedings of the 2011 International Conference on Document Analysis and Recognition
A Model-Based Ruling Line Detection Algorithm for Noisy Handwritten Documents
ICDAR '11 Proceedings of the 2011 International Conference on Document Analysis and Recognition
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Traditionally, document image analysis (DIA) is conducted on datasets that are prepared for research purposes. Many existing handwriting datasets, however, do not necessarily represent the range of problems we wish to solve in real life. In this work, we introduce a noisy and unstructured handwriting dataset that aims for promoting and evaluating robust document analysis algorithms for real-world challenges, as a result of emphasizing the process of building and curating a dataset. First, we explain the data acquisition process and characterize its critical features as noisy and unstructured. Then, we discuss a set of real-world scenarios that might benefit from using our notebook dataset. As an on-going activity, so far we have collected 18 handwritten note-books from nine college students, resulting in a total of 499 pages. We expect to collect over 100 notebooks, or equivalently about 3,000 pages, from at least 50 students. This dataset is available to the research community via the Lehigh document analysis and exploitation (DAE) platform.