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
Shape Matching Using LAT and its Application to Handwritten Numeral Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
The digital atheneum: new approaches for preserving, restoring and analyzing damaged manuscripts
Proceedings of the 1st ACM/IEEE-CS joint conference on Digital libraries
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bidirectional Deformable Matching with Application to Handwritten Character Extraction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Deformable Template Matching within a Bayesian Framework for Hand-Written Graphic Symbol Recognition
GREC '99 Selected Papers from the Third International Workshop on Graphics Recognition, Recent Advances
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Recognition of Persian handwritten digits using image profiles of multiple orientations
Pattern Recognition Letters
Multi-template GAT Correlation for Character Recognition with a Limited Quantity of Data
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Robust Handwritten Character Recognition with Features Inspired by Visual Ventral Stream
Neural Processing Letters
Fast stroke matching by angle quantization
Proceedings of the First International Conference on Immersive Telecommunications
A multiple classifier approach to detect Chinese character recognition errors
Pattern Recognition
Recognition of handwritten Arabic (Indian) numerals using Radon-Fourier-based features
ISPRA'10 Proceedings of the 9th WSEAS international conference on Signal processing, robotics and automation
The use of radon transform in handwritten Arabic (Indian) numerals recognition
WSEAS Transactions on Computers
Deforming the blurred shape model for shape description and recognition
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
A non-rigid appearance model for shape description and recognition
Pattern Recognition
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Deformable models have recently been proposed for many pattern recognition applications due to their ability to handle large shape variations. These proposed approaches represent patterns or shapes as deformable models, which deform themselves to match with the input image, and subsequently feed the extracted information into a classifier. The three components驴modeling, matching, and classification驴are often treated as independent tasks. In this paper, we study how to integrate deformable models into a Bayesian framework as a unified approach for modeling, matching, and classifying shapes. Handwritten character recognition serves as a testbed for evaluating the approach. With the use of our system, recognition is invariant to affine transformation as well as other handwriting variations. In addition, no preprocessing or manual setting of hyperparameters (e.g., regularization parameter and character width) is required. Besides, issues on the incorporation of constraints on model flexibility, detection of subparts, and speed-up are investigated. Using a model set with only 23 prototypes without any discriminative training, we can achieve an accuracy of 94.7 percent with no rejection on a subset (11,791 images by 100 writers) of handwritten digits from the NIST SD-1 dataset.