ICDAR 2019 Competition on Image Retrieval for Historical Handwritten Documents This competition investigates the performance of largescale retrieval of historical document images based on writing style. Based on large image data sets provided by cultural heritage institutions and digital libraries, providing a total of 20 000 document images representing about 10 000 writers, divided in three types: writers of (i) manuscript books, (ii) letters, (iii) charters and legal documents. We focus on the task of automatic image retrieval to simulate common scenarios of humanities research, such as writer retrieval. The most teams submitted traditional methods not using deep learning techniques. The competition results show that a combination of methods is outperforming single methods. Furthermore, letters are much more difficult to retrieve than manuscripts.

ICDAR2019 Competition on Recognition of Early Indian Printed Documents xe2x80x93 REID2019 This paper presents an objective comparative evaluation of page analysis and recognition methods for historical documents with text mainly in Bengali language and script. It describes the competition rules, dataset, and evaluation methodology. Results are presented for five methods  three submitted, one rerun, and one open source stateoftheart system. The focus is on optical character recognition (OCR) performance. Different evaluation metrics were used to gain an insight into the algorithms, including new character accuracy metrics to better reflect the difficult circumstances presented by the documents. The results indicate that deep learning approaches are promising, but there are still significant challenges for historic material of this nature.