ICDAR2019 Competition on Historical Book Analysis  HBA2019 In this paper, we present an evaluative study of pixellabeling methods using the HBA 1.0 dataset for historical book analysis. This study is held in the context of the 2nd historical book analysis (HBA2019) competition and in conjunction with the 15th IAPR international conference on document analysis and recognition (ICDAR2019). The HBA2019 competition provides a large experimental corpus and a thorough evaluation protocol to ensure an objective performance benchmarking of pixellabeling document image methods. Two nested challenges are evaluated in the HBA2019 competition: Challenge 1 and Challenge 2. Challenge 1 evaluates how image analysis methods could discriminate the textual content from the graphical ones at pixel level. Challenge 2 assesses the capabilities of pixellabeling methods to separate the textual content according to different text fonts (e.g. lowercase, uppercase, italic, etc.) at pixel level. During the competition, we received 52 and 38 different teamsu0027 registrations for Challenge 1 and Challenge 2, respectively and finally 5 of them submitted their results in each challenge. Qualitative and numerical results of the participating methods in both challenges are reported and discussed in this paper in order to provide a baseline for future evaluation studies in historical document image analysis. The evaluation shows that the method submitted by the NLPRCASIA team achieves the highest performance in both challenges.

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xe2x80x93 REID2019 This paper presents an objective comparative evaluation of page analysis and recognition methods for historical documents with text mainly in Bengali language and script.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.