Handwritten digit recognition is an important and challenging problem in pattern recognition. This paper reports on experiments done on the MNIST set of handwritten digits, using two different feature spaces and a variety of classifiers on each space. Performance is compared to benchmarks in the field. Table of

The problem of handwritten digit recognition is tackled by multi-layer feedforward neural networks with different types of neuronal activation functions. Three types of activation functions are adopted in the network, namely, the traditional sigmoid function, the sinusoidal function and a periodic function that can be considered as a combination of the first two functions. To speed up the learn...

Handwritten Hindi digit recognition plays an important role in eastern Arab countries especially in the courtesy amounts of Arab bank checks. In this paper, we proposed an efficient offline handwritten Hindi digits recognition system and developed using Multilayer Perceptron Neural Network (MLP). The implemented system recognizes separated handwritten Hindi digits scanned using a scanner. The s...

It is herein proposed a handwritten digit recognition system which biologically inspired of the large-scale structure of the mammalian neocortex. Hierarchical Temporal Memory (HTM) is a memory-prediction network model that takes advantage of the Bayesian belief propagation and revision techniques. In this article a study has been conducted to train a HTM network to recognize handwritten digits ...

Handwritten digit recognition has always been a challenging task in pattern recognition area. In this paper we explore the performance of support vector machines (SVM) and principal component analysis (PCA) on handwritten digits recognition. The performance of SVM on handwritten digits recognition task is compared with three typical classification methods, i.e., linear discriminant classifiers ...

Off-line recognition of text plays a significant role in several applications, such as cheque verification and mail sorting. However, the selection of the technique for feature extraction remains a big challenging step for achieving high recognition accuracy. This paper presents an efficient handwritten digit recognition system based on HOG to capture the discriminative features of digit image....

Binary weights are favored in electronic and optical hardware implementations of neural networks as they lead to improved system speeds. Optical neural networks based on fast ferroelectric liquid crystal binary level devices can beneet from the many orders of magnitudes improved liquid crystal response times. An optimized learning algorithm for all-positive perceptrons is simulated on a limited...

Abstract: The objective of the paper is to recognize handwritten samples of basic Bangla characters using Tesseract open source Optical Character Recognition (OCR) engine under Apache License 2.0. Handwritten data samples containing isolated Bangla basic characters and digits were collected from different users. Tesseract is trained with user-specific data samples of document pages to generate ...