Exploiting Insertion Symbols for Marginal Additions in the Recognition Process to Establish Reading Order
Daniel Stökl Ben Ezra, Bronson Brown-deVost, Pawel Jablonski · Lecture notes in computer science · 2021
In modern and medieval manuscripts, a frequent phenomenon is additions or corrections that are marginal or interlinear with regard to the main text-blocks. With the recent success of Long-Short-Term-Memory Neural Networks (LSTM) in Handwritten-Text-Recognition (HTR) systems, many have chosen lines as the primary structural unit. Due to this approach, establishing the reading order for such additions becomes a non-trivial problem, because they must be inserted between words inside line-units at undefined locations. Even a perfect reading order detection system ordering all lines of a text in the correct order would not be able to deal with inline insertions. The present paper proposes to include markers for the insertion points in the recognition training process, those indicators can then teach the recognition models themselves to detect scribal insertion markers for marginal or interlinear additions.