The earliest handwriting recognition approaches date back to the eighties, when the first attempts of automatically recognizing handwritten words were proposed, e.g., in Mori et al. (1984), Burr (1983), or Bozinovic and Srihari (1989). However, it is only in the mid nineties that the domain takes off thanks to two main factors (Vinciarelli, 2002): on one hand, the diffusion of cheap image acqui...

Finding similar images to a given query image can be computed by different distancemeasures.One of the general distancemeasures is the Earth Mover’s Distance (EMD). Although EMD has proven its ability to retrieve similar images in an average precision of around 95%, high execution time is itsmajor drawback. Embedding EMD into L 1 is a solution that solves this problem by sacrificing performance...

This paper deals with an optical character recognition (OCR) system for handwritten Gujarati numbers. One may find so much of work for Indian languages like Hindi, Kannada, Tamil, Bangala, Malayalam, Gurumukhi etc, but Gujarati is a language for which hardly any work is traceable especially for handwritten characters. Here in this work a neural network is proposed for Gujarati handwritten digit...

Images of handwritten digits are different from natural images as the orientation a digit, well similarity features digits, makes confusion. On other hand, deep convolutional neural networks achieving huge success in computer vision problems, especially image classification. Here, we propose task-oriented model called Bengali numeral digit recognition based on densely connected (BDNet). BDNet i...

Over the last decades, most approaches proposed for handwritten digit string recognition (HDSR) have resorted to segmentation, which is dominated by heuristics, thereby imposing substantial constraints on final performance. Few of them been based segmentation-free strategies where each pixel column has a potential cut location. Recently, added another perspective problem, leading promising resu...

The writing style of the same writer varies from instance to in Arabic and English handwritten digit recognition, making recognition challenging. Currently, deep learning approaches are applied many applications, including convolutional neural networks (CNNs) modified produce other models, such as local binary (LBCNNs). An LBCNN is created by fusing a pattern (LBP) with CNN reformulating LBP co...

We investigate techniques to combine multiple representations of a handwritten digit to increase classification accuracy without significantly increasing system complexity or recognition time. In pen-based recognition, the input is the dynamic movement of the pentip over the pressure sensitive tablet. There is also the image formed as a result of this movement. On a real-world database of handw...

Handwritten digit recognition is a task of great importance in many applications. There are different challenges faced while attempting to solve this problem. It has drawn much attention from the field of machine learning and pattern recognition. Minimax probability machine (MPM) is a novel method in machine learning and data mining. In this paper, we present an extension algorithm for MPM, whi...

Convolutional neural networks provide an eecient method to constrain the complexity of feedforward neural networks by weightsharing. This network topology has been applied in particular to image classiication when raw images are to be classi-ed without preprocessing. In this paper two variations of convolutional networks-Neocognitron and Neoperceptron-are compared with classiiers based on fully...