ICDAR 2019 Competition on Image Retrieval for Historical Handwritten Documents
This addresses the problem of automating writer retrieval for humanities research, but it is incremental as it builds on existing methods without major breakthroughs.
The competition tackled large-scale retrieval of historical handwritten document images by writing style, using 20,000 images from 10,000 writers, and found that combined methods outperformed single ones, with letters being more difficult to retrieve than manuscripts.
This competition investigates the performance of large-scale retrieval of historical document images based on writing style.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.