On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Properties of Embedding Methods for Similarity Searching in Metric Spaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Scanning n-tuple Classifier for Online Recognition of Handwritten Digits
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A New Fuzzy Geometric Representation for On-Line Isolated Character Recognition
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
BoostMap: a method for efficient approximate similarity rankings
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Learning a discriminative classifier using shape context distances
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
BoostMap: An Embedding Method for Efficient Nearest Neighbor Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Experimental comparison of representation methods and distance measures for time series data
Data Mining and Knowledge Discovery
| Hi-index | 0.00 |
Nearest neighbor classifiers are simple to implement, yet they can model complex non-parametric distributions, and provide state-of-the-art recognition accuracy in OCR databases. At the same time, they may be too slow for practical character recognition, especially when they rely on similarity measures that require computationally expensive pairwise alignments between characters. This paper proposes an efficient method for computing an approximate similarity score between two characters based on their exact alignment to a small number of prototypes. The proposed method is applied to both online and offline character recognition, where similarity is based on widely used and computationally expensive alignment methods, i.e., Dynamic Time Warping and the Hungarian method respectively. In both cases significant recognition speedup is obtained at the expense of only a minor increase in recognition error.