The Workshop

In the recent past, there have been increased efforts worldwide to preserve our cultural heritage conveyed in historical documents. The 4th International Workshop on Historical Document Imaging and Processing (HIP’17) brings together researchers from various fields working on image acquisition, restoration, indexing, and retrieval to make these documents accessible in digital libraries.

HIP’17 is held in conjunction with ICDAR’17 in Kyoto, following the ICDAR satellite workshops HIP’11 in Beijing, HIP’13 in Washington, and HIP’15 in Nancy.

The workshop aims to provide researchers with a forum that is complementary and synergetic to the main sessions at ICDAR on document analysis and recognition. The manifold topics addressed in this workshop encompass the entire processing chain from image acquisition to information extraction. We include the growing importance of machine learning in this processing chain, such as convolutional and recurrent neural networks, and we also encourage the presentation of entire projects in the context of historical documents. For a detailed list of topics, please refer to the call for papers.

Image source: St. Gallen, Stiftsbibliothek, Cod. Sang. 857 (DOI: 10.5076/e-codices-csg-0857)

Thanks to each and everyone for contributing to a successful HIP 2017!

And special thanks to Apostolos Antonacopoulos for agreeing to serve as chair for the next workshop. Hope to see you in Sydney, Australia, for HIP 2019.


IAPR Best Paper Award

The IAPR Best Paper Award of HIP 2017 is presented to

Hung Tuan Nguyen, Nam Tuan Ly, Kha Cong Nguyen, Cuong Tuan Nguyen and Masaki Nakagawa

for their paper entitled

Attempts to recognize anomalously deformed Kana in Japanese historical documents

We congratulate the authors for their outstanding work, which is breaking ground for the challenging problem of Kana recognition in historical Japanese documents, thoroughly investigates state-of-the-art methods at different recognition levels, and promotes the use of open data for historical document imaging and processing.

Andreas Fischer, Angelika Garz, Kengo Terasawa, and Bill Barrett
IAPR Best Paper Award Committee