Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
Handwritten digit recognition with a back-propagation network
Advances in neural information processing systems 2
The nature of statistical learning theory
The nature of statistical learning theory
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognition of English and Arabic Numerals Using a Dynamic Number of Hidden Neurons
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
Recognition of Persian handwritten digits using image profiles of multiple orientations
Pattern Recognition Letters
Object Recognition with Features Inspired by Visual Cortex
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Introducing a very large dataset of handwritten Farsi digits and a study on their varieties
Pattern Recognition Letters
Robust Object Recognition with Cortex-Like Mechanisms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Deformation Models for Image Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Permutation Coding Technique for Image Recognition Systems
IEEE Transactions on Neural Networks
Augmenting the generalized hough transform to enable the mining of petroglyphs
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Online learning of task-driven object-based visual attention control
Image and Vision Computing
An efficient and effective similarity measure to enable data mining of petroglyphs
Data Mining and Knowledge Discovery
Pose invariant face recognition using biological inspired features based on ensemble of classifiers
Journal of Visual Communication and Image Representation
A Mathematical Model of Retinal Ganglion Cells and Its Applications in Image Representation
Neural Processing Letters
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This paper focuses on the applicability of the features inspired by the visual ventral stream for handwritten character recognition. A set of scale and translation invariant C2 features are first extracted from all images in the dataset. Three standard classifiers kNN, ANN and SVM are then trained over a training set and then compared over a separate test set. In order to achieve higher recognition rate, a two stage classifier was designed with different preprocessing in the second stage. Experiments performed to validate the method on the well-known MNIST database, standard Farsi digits and characters, exhibit high recognition rates and compete with some of the best existing approaches. Moreover an analysis is conducted to evaluate the robustness of this approach to orientation, scale and translation distortions.