An OCR Pipeline and Semantic Text Analysis for Comics - Hartel, Rita; Dunst, Alexander (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2021)
Optical character recognition has remained a challenge for comics, given the high variability of placement of text on the page, the wide variety of frequently handwritten fonts, and the limited availability and small size of datasets. This paper reports on currently on-going work on an OCR pipeline that includes text spotting with the help of a U-Net based fully convolutional neural network and OCR training with the open-source software Calamari, which was performed on the “Graphic Narrative Corpus” of book-length graphic novels written in English. Based on the results of the OCR training, we then present an analysis of the textual properties of 129 graphic novels correlated with page length, historical development, and genre affiliation.
CRIS-Team Scopus Importer:2023-03-09
Keywords: Comics; Graphic novels; OCR; Semantic analysis; Text spotting