ICDAR2017 Competition on Recognition of Early Indian Printed Documents - REID2017
Christian Clausner, Apostolos Antonacopoulos, Tom Derrick, Stefan Pletschacher · 2017
This paper presents an objective comparative evaluation of page analysis and recognition methods for historical documents with text mainly in Bengali language and script.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 de-scribes the competition (modus operandi, dataset and evaluation methodology) held in the context of ICDAR2017, presenting the results of the evaluation of seven methods – three sub-mitted and four variations of open source state-of-the-art systems. 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 the most promising, but there is still a considerable need to develop robust methods that deal with challenges of historic material of this nature.