ICDAR 2024 Competition on Multi Font Group Recognition and OCR - van der Loop, Janne; Kordon, Florian; Mayr, Martin; Christlein, Vincent; Wu, Fei; Rodriguez Salas, Dalia; Weichselbaumer, Nikolaus; Seuret, Mathias (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2024)
This competition investigates the performance of several methods for two types of analyses of early modern prints: (1) optical character recognition, and (2) font recognition at the character level. We have created and published a novel dataset that contains the ground truth for both tasks. The dataset has been carefully curated and annotated by an expert with several years of expertise in transcribing early modern prints. Both tasks involved two distinct tracks, differing in ground truth management: one that only allows the participants to use the provided data for model training and a second that removes this restriction. Out of the five participating teams, four participated in the first track, and three in the second one. The best team reached a text Character Error Rate (CER) of 0.82 % and a font CER of 2.96 % for the first track. In the second track, these numbers could be slightly improved to 0.81 % text CER and 2.78 % font CER.
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Keywords: Document analysis; Font recognition; Historical documents; Optical character recognition