A General Framework for the Recognition of Online Handwritten Graphics
Frank Julca-Aguilar, Harold Mouch\`ere, Christian Viard-Gaudin and, Nina S. T. Hirata

TL;DR
This paper introduces a versatile graph-based framework for recognizing online handwritten graphics, effectively integrating symbol and structural information, adaptable to various graphic types, and balancing recognition accuracy with computational efficiency.
Contribution
The framework models graphics as labeled graphs and formulates recognition as a graph parsing problem, enabling flexible and accurate interpretation of diverse handwritten graphics.
Findings
Achieves state-of-the-art accuracy in mathematical expression recognition
Successfully applies to flowchart recognition tasks
Offers a flexible approach adaptable to different graphic notations
Abstract
We propose a new framework for the recognition of online handwritten graphics. Three main features of the framework are its ability to treat symbol and structural level information in an integrated way, its flexibility with respect to different families of graphics, and means to control the tradeoff between recognition effectiveness and computational cost. We model a graphic as a labeled graph generated from a graph grammar. Non-terminal vertices represent subcomponents, terminal vertices represent symbols, and edges represent relations between subcomponents or symbols. We then model the recognition problem as a graph parsing problem: given an input stroke set, we search for a parse tree that represents the best interpretation of the input. Our graph parsing algorithm generates multiple interpretations (consistent with the grammar) and then we extract an optimal interpretation according…
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