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In this study, we investigated the effects of mastering multiple scripts in handwritten character recognition by means of computational simulations. In particular, we trained a set of deep neural networks on two different datasets of handwritten characters: the HODA dataset, which is a collection of images of handwritten Persian digits, and the MNIST dataset, which contains Latin handwritten digits...
The contribution of this paper is to bridge the gap on understanding the mathematical structure and the computational implementation of a convolutional neural network using a minimal model. The proposed minimal convolutional neural network is presented using a layering approach. This approach provides a clear understanding of the main mathematical operations in a convolutional neural network. Hence,...
The contribution of this paper is to bridge the gap on understanding the mathematical structure and the computational implementation of a convolutional neural network (CNN) using a minimal model (Minimal CNN). The proposed minimal CNN is presented using a layering approach. This approach provides a concise and accessible understanding of the main mathematical operations of a CNN. Hence, it benefits...
This paper deals with handwriting recognition (HWR) using artificial intelligence of so-called Comenia script — a modern handwritten font similar to block letters recently introduced at primary schools in the Czech Republic. This work describes a method how to extend a limited training set of handwritten letters and proposes a new method to increase stability and accuracy by artificially created image...
As a typical deep learning method, Deep Belief Network (DBN) and Dropout method are usually used together for pattern recognition in case of lacking training data. Dropout training can avoid the overfitting phenomenon in deep neural network. During the testing stage, the outputs of all neurons in hidden layers are multiplied by a same factor as their actual outputs in the original Dropout method....
Handwritten character recognition is an active area of research with applications in numerous fields. Past and recent works in this field have concentrated on various languages. Arabic is one language where the scope of research is still widespread, with it being one of the most popular languages in the world and being syntactically different from other major languages. Das et al. [1] has pioneered...
Deep Convolutional Neural Networks - also known as DCNN - are powerful models for different visual pattern classification problems. Many works in this field use image augmentation at the training phase to achieve better accuracy. This paper presents blocky artifact as an augmentation technique to increase the accuracy of DCNN for handwritten digit recognition, both English and Bangla digits, i.e.,...
This paper reports a new approach based on convolutional neural networks (CNNs), which uses spatial transformer networks (STNs). The approach, referred to as Tied Spatial Transformer Networks (TSTNs), consists of training a system which combines a localization CNN and a classification CNN whose weights are shared. The localization CNN is used for predicting an affine transform for the input image,...
Automatic handwriting recognition of digits and digit strings, are of real interest commercially and as an academic research topic. Recent advances using neural networks and especially deep learning algorithms such as convolutional neural nets present impressive results for single digit recognition. Such results enable developing efficient tools for automatic mail sorting and reading amounts and dates...
As a machine learning algorithms, deep learning algorithms developed in recent years, have been successfully practiced in many fields of computer vision, like face recognition, object detection and image classification. These Deep algorithms look for drawing out a very performing representation of the data, among which image and speech, through multi-layers in a deep hierarchical structure. In this...
Handwriting recognition is the ability of a computer to understand handwritten inputs from users. Generally it includes preprocessing, feature extraction, and classifier training. In this paper, we will develop a handwriting digit recognition system by using Deep Boltzmann Machine (DBM) together with the Support Vector Machine (SVM). DBM is a deep learning technique to learn high level features from...
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