READ-BAD: A New Dataset and Evaluation Scheme for Baseline Detection in Archival Documents
This work addresses the challenge of baseline detection for archival document analysis, offering a dataset and evaluation method that could improve text recognition in historical contexts, though it appears incremental as it builds on existing evaluation schemes.
The authors tackled the problem of text line detection in archival documents by introducing a new dataset with 2036 images featuring varied layouts and degradations, and proposed a baseline-based evaluation scheme that eliminates the need for binarization and handles skewed/rotated text lines, with results presented from a recent algorithm.
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 ground truth 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 and it can handle skewed as well as rotated text lines. The ICDAR 2017 Competition on Baseline Detection and the ICDAR 2017 Competition on Layout Analysis for Challenging Medieval Manuscripts used this evaluation scheme. Finally, we present results achieved by a recently published text line detection algorithm.