The nature of statistical learning theory
The nature of statistical learning theory
A Feature for Character Recognition Based on Directional Distance Distributions
ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
Neural versus Syntactic Recognition of Handwritten Numerals
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
On the Performance of Wavelets for Handwritten Numerals Recognition
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Directional Pattern Matching for Character Recognition Revisited
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Principal Component Analysis for Online Handwritten Character Recognition
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
A Multi-View Approach on Modular PCA for Illumination and Pose Invariant Face Recognition
AIPR '04 Proceedings of the 33rd Applied Imagery Pattern Recognition Workshop
An improved handwritten Chinese character recognition system using support vector machine
Pattern Recognition Letters - Special issue: Artificial neural networks in pattern recognition
A trainable feature extractor for handwritten digit recognition
Pattern Recognition
Face Recognition Using Improved Fast PCA Algorithm
CISP '08 Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 1 - Volume 01
Handwritten Numeral Databases of Indian Scripts and Multistage Recognition of Mixed Numerals
IEEE Transactions on Pattern Analysis and Machine Intelligence
Handwritten character recognition using elastic matching and PCA
Proceedings of the International Conference on Advances in Computing, Communication and Control
Handwritten numeral recognition based on simplified structural classification and fuzzy memberships
Expert Systems with Applications: An International Journal
Recognition of Numeric Postal Codes from Multi-script Postal Address Blocks
PReMI '09 Proceedings of the 3rd International Conference on Pattern Recognition and Machine Intelligence
A Novel Domain-Specific Feature Extraction Scheme for Arabic Handwritten Digits Recognition
ICMLA '09 Proceedings of the 2009 International Conference on Machine Learning and Applications
Handwritten character recognition through two-stage foreground sub-sampling
Pattern Recognition
PCA based immune networks for human face recognition
Applied Soft Computing
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
A hybrid expert system approach for telemonitoring of vocal fold pathology
Applied Soft Computing
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Principal Component Analysis (PCA) and Modular PCA (MPCA) are well known statistical methods for recognition of facial images. But only PCA/MPCA is found to be insufficient to achieve high classification accuracy required for handwritten character recognition application. This is due to the shortcomings of those methods to represent certain local morphometric information present in the character patterns. On the other hand Quad-tree based hierarchically derived Longest-Run (QTLR) features, a type of popularly used topological features for character recognition, miss some global statistical information of the characters. In this paper, we have introduced a new combination of PCA/MPCA and QTLR features for OCR of handwritten numerals. The performance of the designed feature-combination is evaluated on handwritten numerals of five popular scripts of Indian sub-continent, viz., Arabic, Bangla, Devanagari, Latin and Telugu with Support Vector Machine (SVM) based classifier. From the results it has been observed that MPCA+QTLR feature combination outperforms PCA+QTLR feature combination and most other conventional features available in the literature.