Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
MathPad2: a system for the creation and exploration of mathematical sketches
ACM SIGGRAPH 2004 Papers
SketchREAD: a multi-domain sketch recognition engine
Proceedings of the 17th annual ACM symposium on User interface software and technology
Recognition and Grouping of Handwritten Text in Diagrams and Equations
IWFHR '04 Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition
Shallow parsing with conditional random fields
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Learning Diagram Parts with Hidden Random Fields
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Sketch based interfaces: early processing for sketch understanding
ACM SIGGRAPH 2006 Courses
LADDER: a language to describe drawing, display, and editing in sketch recognition
ACM SIGGRAPH 2006 Courses
Computer Vision and Image Understanding
Envisioning sketch recognition: a local feature based approach to recognizing informal sketches
Envisioning sketch recognition: a local feature based approach to recognizing informal sketches
PaleoSketch: accurate primitive sketch recognition and beautification
Proceedings of the 13th international conference on Intelligent user interfaces
Sketch recognition in interspersed drawings using time-based graphical models
Computers and Graphics
Interpretation of molecule conformations from drawn diagrams
Interpretation of molecule conformations from drawn diagrams
Revisiting ShortStraw: improving corner finding in sketch-based interfaces
Proceedings of the 6th Eurographics Symposium on Sketch-Based Interfaces and Modeling
Recognition of hand drawn chemical diagrams
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Combining geometry and domain knowledge to interpret hand-drawn diagrams
Computers and Graphics
A visual approach to sketched symbol recognition
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Loopy belief propagation for approximate inference: an empirical study
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
PhysicsBook: a sketch-based interface for animating physics diagrams
Proceedings of the 2012 ACM international conference on Intelligent User Interfaces
ACM Transactions on Graphics (TOG) - SIGGRAPH 2012 Conference Proceedings
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We describe a new sketch recognition framework for chemical structure drawings that combines multiple levels of visual features using a jointly trained conditional random field. This joint model of appearance at different levels of detail makes our framework less sensitive to noise and drawing variations, improving accuracy and robustness. In addition, we present a novel learning-based approach to corner detection that achieves nearly perfect accuracy in our domain. The result is a recognizer that is better able to handle the wide range of drawing styles found in messy freehand sketches. Our system handles both graphics and text, producing a complete molecular structure as output. It works in real time, providing visual feedback about the recognition progress. On a dataset of chemical drawings our system achieved an accuracy rate of 97.4%, an improvement over the best reported results in literature. A preliminary user study also showed that participants were on average over twice as fast using our sketch-based system compared to ChemDraw, a popular CAD-based tool for authoring chemical diagrams. This was the case even though most of the users had years of experience using ChemDraw and little or no experience using Tablet PCs.