ICDAR 2019 Historical Document Reading Challenge on Large Structured Chinese Family Records In this paper, we present a large historical database of Chinese family records with the aim to develop robust systems for historical document analysis. In this direction, we propose a Historical Document Reading Challenge on Large Chinese Structured Family Records (ICDAR 2019 HDRCCHINESE). The objective of the competition is to recognize and analyze the layout, and finally detect and recognize the textlines and characters of the large historical document image dataset containing more than 100000 pages. Cascade RCNN, CRNN, and UNet based architectures were trained to evaluate the performances in these tasks. Error rate of 0.01 has been recorded for textline recognition (Task1) whereas a Jaccard Index of 99:54% has been recorded for layout analysis (Task2). The graph edit distance based total error ratio of 1:5% has been recorded for complete integrated textline detection and recognition (Task3).

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REID2019 This paper presents an objective comparative evaluation of page analysis and recognition methods for historical documents with text mainly in Bengali language and script.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