Historical Text Line Segmentation Using Deep Learning Algorithms: Mask-RCNN against U-Net Networks.

ABSTRACT: Text line segmentation is a necessary preliminary step before most text transcription algorithms are applied. The leading deep learning networks used in this context (ARU-Net, dhSegment, and Doc-UFCN) are based on the U-Net architecture. They are efficient, but fall under the same concept, requiring a post-processing step to perform instance (e.g., text line) segmentation. In the present work, we test the advantages of Mask-RCNN, which is designed to perform instance segmentation directly. This work is the first to directly compare Mask-RCNN- and U-Net-based networks on text segmentation of historical documents, showing the superiority of the former over the latter. Three studies were conducted, one comparing these networks on different historical databases, another comparing Mask-RCNN wit

SUBMITTER: Fizaine FC 

PROVIDER: S-EPMC10971631 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature