Thomas Frick, Cezary Skura, et al.
CVPR 2024
A framework of data organization methods and corresponding recognition results for UNIPEN databases is presented to enable the comparison of recognition results from different isolated character recognizers. A reproducible method for splitting the Train-R01/V07 data into an array of multi-writer and omni-writer training and testing pairs is proposed. Recognition results and uncertainties are provided for each pair, as well as results for the DevTest-R01/V02 character subsets, using an online scanning n-tuple recognizer. Several other published results are surveyed within this context. In sum, this report provides the reader multiple points of reference useful for comparing a number of published recognition results and a proposed framework that similarly allows private evaluation of unpublished recognition results.
Thomas Frick, Cezary Skura, et al.
CVPR 2024
Weiming Hu, Nianhua Xie, et al.
IEEE TPAMI
Eugene H. Ratzlaff
ICDAR 2001
Tanveer Syeda-Mahmood, Dragutin Petkovic
Signal Processing: Image Communication