Simulated annealing: theory and applications
Simulated annealing: theory and applications
Graphics recognition—general context and challenges
Pattern Recognition Letters
Using Generative Models for Handwritten Digit Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Representation and Recognition of Handwritten Digits Using Deformable Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
A system to understand hand-drawn floor plans using subgraph isomorphism and Hough transform
Machine Vision and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Deformable template models: a review
Signal Processing - Special issue on deformable models and techniques for image and signal processing
Automatic Learning and Recognition of Graphical Symbols in Engineering Drawings
Selected Papers from the First International Workshop on Graphics Recognition, Methods and Applications
Graphic Symbol Recognition: An Overview
GREC '97 Selected Papers from the Second International Workshop on Graphics Recognition, Algorithms and Systems
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
Hand-Drawn Shape Recognition Using the SVM'ed Kernel
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
Fuzzy intervals for designing structural signature: an application to graphic symbol recognition
GREC'09 Proceedings of the 8th international conference on Graphics recognition: achievements, challenges, and evolution
GREC'05 Proceedings of the 6th international conference on Graphics Recognition: ten Years Review and Future Perspectives
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We describe a method for hand-drawn symbol recognition based on deformable template matching able to handle uncertainty and imprecision inherent to hand-drawing. Symbols are represented as a set of straight lines and their deformations as geometric transformations of these lines. Matching, however, is done over the original binary image to avoid loss of information during line detection. It is defined as an energy minimization problem, using a Bayesian framework which allows to combine fidelity to ideal shape of the symbol and flexibility to modify the symbol in order to get the best fit to the binary input image. Prior to matching, we find the best global transformation of the symbol to start the recognition process, based on the distance between symbol lines and image lines. We have applied this method to the recognition of dimensions and symbols in architectural floor plans and we show its flexibility to recognize distorted symbols.