Toward speech text recognition for comic books

Speech text in comic books is placed and written in a particular manner by the letterers which raises unusual challenges for text recognition. We first detail these challenges and present different approaches to solve them. We compare the performances of generic versus specifically trained OCR systems for typewritten and handwritten text lines from French comic books. This work is evaluated over a subset of public (eBDtheque) and private (Sequencity) datasets. We demonstrate that generic OCR systems perform best on typewritten-like and lowercase fonts while specifically trained OCR can be very powerful on skewed, uppercase and even cursive fonts.