In real-world applications new data patterns and categories that were not covered by the
training data can frequently emerge necessitating the capability to detect and adapt to novel
characters incrementally. Researchers refer to these challenges as the Open-Set Text
Recognition (OSTR) task which has in recent years emerged as one of the prominent issues in
the field of text recognition. This book begins by providing an introduction to the background
of the OSTR task covering essential aspects such as open-set identification and recognition
conventional OCR methods and their applications. Subsequently the concept and definition of
the OSTR task are presented encompassing its objectives use cases performance metrics
datasets and protocols. A general framework for OSTR is then detailed composed of four key
components: The Aligned Represented Space the Label-to-Representation Mapping the
Sample-to-Representation Mapping and the Open-set Predictor. In addition possible
implementations of each module within the framework are discussed. Following this two specific
open-set text recognition methods OSOCR and OpenCCD are introduced. The book concludes by
delving into applications and future directions of Open-set text recognition tasks.This book
presents a comprehensive overview of the open-set text recognition task including concepts
framework and algorithms. It is suitable for graduated students and young researchers who are
majoring in pattern recognition and computer science especially interdisciplinary research.
