Algorithms for clustering data
Algorithms for clustering data
The design and analysis of spatial data structures
The design and analysis of spatial data structures
Machine Learning
Representation and Recognition of Handwritten Digits Using Deformable Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
OCR in a Hierarchical Feature Space
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Introduction to Digital Image Processing
An Introduction to Digital Image Processing
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
A Database for Handwritten Text Recognition Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Recognition of Handwritten Numerical Strings: A Recognition and Verification Strategy
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine and Human Recognition of Segmented Characters from Handwritten Words
ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
IWFHR '04 Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition
Application of Fuzzy Logic to Online Recognition of Handwritten Symbols
IWFHR '04 Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition
Character Representation and Recognition Using Quadtree-based Fractal Encoding Scheme
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 02
SVM-based hierarchical architectures for handwritten Bangla character recognition
International Journal on Document Analysis and Recognition
Database-driven mathematical character recognition
GREC'05 Proceedings of the 6th international conference on Graphics Recognition: ten Years Review and Future Perspectives
An overview of character recognition focused on off-line handwriting
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Handwritten word recognition with character and inter-character neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A statistical-topological feature combination for recognition of handwritten numerals
Applied Soft Computing
Handwritten character recognition system using a simple feature
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
An efficient way of combining SVMs for handwritten digit recognition
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
A MDRNN-SVM hybrid model for cursive offline handwriting recognition
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
A multiple feature vector framework for forest species recognition
Proceedings of the 28th Annual ACM Symposium on Applied Computing
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In this paper, we present a methodology for off-line handwritten character recognition. The proposed methodology relies on a new feature extraction technique based on recursive subdivisions of the character image so that the resulting sub-images at each iteration have balanced (approximately equal) numbers of foreground pixels, as far as this is possible. Feature extraction is followed by a two-stage classification scheme based on the level of granularity of the feature extraction method. Classes with high values in the confusion matrix are merged at a certain level and for each group of merged classes, granularity features from the level that best distinguishes them are employed. Two handwritten character databases (CEDAR and CIL) as well as two handwritten digit databases (MNIST and CEDAR) were used in order to demonstrate the effectiveness of the proposed technique. The recognition result achieved, in comparison to the ones reported in the literature, is the highest for the well-known CEDAR Character Database (94.73%) and among the best for the MNIST Database (99.03%)