Journal ArticleDOI
Handwritten digit recognition: benchmarking of state-of-the-art techniques
Reads0
Chats0
TLDR
The results of handwritten digit recognition on well-known image databases using state-of-the-art feature extraction and classification techniques are competitive to the best ones previously reported on the same databases.About:
This article is published in Pattern Recognition.The article was published on 2003-10-01. It has received 545 citations till now. The article focuses on the topics: Feature (computer vision) & Linear classifier.read more
Citations
More filters
Journal ArticleDOI
A survey of the recent architectures of deep convolutional neural networks
TL;DR: Deep Convolutional Neural Networks (CNNs) as mentioned in this paper are a special type of Neural Networks, which has shown exemplary performance on several competitions related to Computer Vision and Image Processing.
Journal ArticleDOI
Handwritten Numeral Databases of Indian Scripts and Multistage Recognition of Mixed Numerals
TL;DR: P pioneering development of two databases for handwritten numerals of two most popular Indian scripts, a multistage cascaded recognition scheme using wavelet based multiresolution representations and multilayer perceptron classifiers and application for the recognition of mixed handwritten numeral recognition of three Indian scripts Devanagari, Bangla and English.
Journal ArticleDOI
Handwritten digit recognition: investigation of normalization and feature extraction techniques
TL;DR: A comparison of normalization functions shows that moment-based functions outperform the dimension-based ones and the aspect ratio mapping is influential and the comparison of feature vectors shows that the improved feature extraction strategies outperform their baseline counterparts.
Proceedings ArticleDOI
TI-POOLING: Transformation-Invariant Pooling for Feature Learning in Convolutional Neural Networks
TL;DR: A deep neural network topology that incorporates a simple to implement transformationinvariant pooling operator (TI-POOLING) that is able to efficiently handle prior knowledge on nuisance variations in the data, such as rotation or scale changes is presented.
Proceedings ArticleDOI
Oriented Response Networks
TL;DR: Active rotating filters (ARFs) as mentioned in this paper can be used to produce within-class rotation-invariant deep features while maintaining inter-class discrimination for classification tasks, which can also be used for image and object orientation estimation.
References
More filters
Book
The Nature of Statistical Learning Theory
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
Journal ArticleDOI
Learning representations by back-propagating errors
TL;DR: Back-propagation repeatedly adjusts the weights of the connections in the network so as to minimize a measure of the difference between the actual output vector of the net and the desired output vector, which helps to represent important features of the task domain.
Book
Neural networks for pattern recognition
TL;DR: This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition, and is designed as a text, with over 100 exercises, to benefit anyone involved in the fields of neural computation and pattern recognition.
Journal ArticleDOI
A Tutorial on Support Vector Machines for Pattern Recognition
TL;DR: There are several arguments which support the observed high accuracy of SVMs, which are reviewed and numerous examples and proofs of most of the key theorems are given.