READ-BAD: A New Dataset and Evaluation Scheme for Baseline Detection in Archival Documents
Tobias Grüning, Roger Labahn, Markus Diem, Florian Kleber, Stefan Fiel, TU Wien
Text line detection is crucial for any application associated with Automatic Text Recognition or Keyword Spotting. Modern algorithms perform good on well-established datasets since they either comprise clean data or simple/homogeneous page layouts. We have collected and annotated 2036 archival document images from different locations and time periods. The dataset contains varying page layouts and degradations that challenge text line segmentation methods. Well established text line segmentation evaluation schemes such as the Detection Rate or Recognition Accuracy demand for binarized data that is annotated on a pixel level. Producing groundtruth by these means is laborious and not needed to determine a method's quality. In this paper we propose a new evaluation scheme that is based on baselines. The proposed scheme has no need for binarization, it can handle skewed and rotated text lines and its results correlate with Handwritten Text Recognition accuracy. The ICDAR 2017 Competition on Baseline Detection and the ICDAR 2017 Competition on Layout Analysis for Challenging Medieval Manuscripts make use of this evaluation scheme.