PT Unknown
AU Pau Torras
   Mohamed Ali Souibgui
   Sanket Biswas
   Alicia Fornes
TI Segmentation-Free Alignment of Arbitrary Symbol Transcripts to Images
BT Document Analysis and Recognition – ICDAR 2023 Workshops
PY 2023
BP 83
EP 93
VL 14193
DE Historical Manuscripts; Symbol Alignment
AB Developing arbitrary symbol recognition systems is a challenging endeavour. Even using content-agnostic architectures such as few-shot models, performance can be substantially improved by providing a number of well-annotated examples into training. In some contexts, transcripts of the symbols are available without any position information associated to them, which enables using line-level recognition architectures. A way of providing this position information to detection-based architectures is finding systems that can align the input symbols with the transcription. In this paper we discuss some symbol alignment techniques that are suitable for low-data scenarios and provide an insight on their perceived strengths and weaknesses. In particular, we study the usage of Connectionist Temporal Classification models, Attention-Based Sequence to Sequence models and we compare them with the results obtained on a few-shot recognition system.
ER