The nature of statistical learning theory
The nature of statistical learning theory
Hierarchical parsing and recognition of hand-sketched diagrams
Proceedings of the 17th annual ACM symposium on User interface software and technology
HMM-based efficient sketch recognition
Proceedings of the 10th international conference on Intelligent user interfaces
ChemPad: generating 3D molecules from 2D sketches
SIGGRAPH '05 ACM SIGGRAPH 2005 Posters
LADDER: a language to describe drawing, display, and editing in sketch recognition
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Combining geometry and domain knowledge to interpret hand-drawn diagrams
Computers and Graphics
Spatial recognition and grouping of text and graphics
SBM'04 Proceedings of the First Eurographics conference on Sketch-Based Interfaces and Modeling
A Novel Pen-Based Flowchart Recognition System for Programming Teaching
Advances in Blended Learning
Structuring and manipulating hand-drawn concept maps
Proceedings of the 14th international conference on Intelligent user interfaces
A visual approach to sketched symbol recognition
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Intelligent understanding of handwritten geometry theorem proving
Proceedings of the 15th international conference on Intelligent user interfaces
Multimodal interaction with an autonomous forklift
Proceedings of the 5th ACM/IEEE international conference on Human-robot interaction
QuickDiagram: a system for online sketching and understanding of diagrams
GREC'09 Proceedings of the 8th international conference on Graphics recognition: achievements, challenges, and evolution
ChemInk: a natural real-time recognition system for chemical drawings
Proceedings of the 16th international conference on Intelligent user interfaces
Recognizing sketched multistroke primitives
ACM Transactions on Interactive Intelligent Systems (TiiS)
Understanding, Manipulating and Searching Hand-Drawn Concept Maps
ACM Transactions on Intelligent Systems and Technology (TIST)
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Chemists often use hand-drawn structural diagrams to capture and communicate ideas about organic compounds. However, the software available today for specifying these structures to a computer relies on a traditional mouse and keyboard interface, and as a result lacks the ease of use, naturalness, and speed of drawing on paper. In response, we have developed a novel sketch-based system capable of interpreting hand-drawn organic chemistry diagrams, allowing users to draw molecules with a pen-based input device in much the same way that they would on paper. The system's ability to interpret a sketch is based on knowledge about both chemistry and chemical drawing conventions. The system employs a trainable symbol recognizer incorporating both feature-based and image-based methods to locate and identify symbols in the sketch. Analysis of the spatial context around each symbol allows the system to choose among competing interpretations and determine an initial structure for the molecule. Finally, knowledge of chemistry (in particular chemical valence) enables the system to check the validity of its interpretation and, when necessary, refine it to recover from inconsistencies. We demonstrate that the system is capable of recognizing diagrams of common organic molecules and show that using domain knowledge produces a noticeable improvement in recognition accuracy.