Digit recognition
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Asymmetric Tri-training for Unsupervised Domain Adaptation Open
Deep-layered models trained on a large number of labeled samples boost the accuracy of many tasks. It is important to apply such models to different domains because collecting many labeled samples in various domains is expensive. In unsupe…
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Improved Handwritten Digit Recognition Using Convolutional Neural Networks (CNN) Open
Traditional systems of handwriting recognition have relied on handcrafted features and a large amount of prior knowledge. Training an Optical character recognition (OCR) system based on these prerequisites is a challenging task. Research i…
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Hybrid CNN-SVM Classifier for Handwritten Digit Recognition Open
The aim of this paper is to develop a hybrid model of a powerful Convolutional Neural Networks (CNN) and Support Vector Machine (SVM) for recognition of handwritten digit from MNIST dataset. The proposed hybrid model combines the key prope…
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Multiple classifiers fusion and CNN feature extraction for handwritten digits recognition Open
Handwritten digits recognition has been treated as a multi-class classification problem in the machine learning context, where each of the ten digits (0-9) is viewed as a class and the machine learning task is essentially to train a classi…
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Handwritten Digit Recognition Using Machine Learning Algorithms Open
Handwritten character recognition is one of the practically important issues in pattern recognition applications. The applications of digit recognition include in postal mail sorting, bank check processing, form data entry, etc. The main p…
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Novel feature extraction technique for the recognition of handwritten digits Open
This paper presents an efficient handwritten digit recognition system based on support vector machines (SVM). A novel feature set based on transition information in the vertical and horizontal directions of a digit image combined with the …
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Recognition of Handwritten Digit using Convolutional Neural Network in Python with Tensorflow and Comparison of Performance for Various Hidden Layers Open
In recent times, with the increase of Artificial Neural Network (ANN), deep learning has brought a dramatic twist in the field of machine learning by making it more artificially intelligent. Deep learning is remarkably used in vast ranges …
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DIGITNET: A Deep Handwritten Digit Detection and Recognition Method Using a New Historical Handwritten Digit Dataset Open
This paper introduces a novel deep learning architecture, named DIGITNET, and a large-scale digit dataset, named DIDA, to detect and recognize handwritten digits in historical document images written in the nineteen century. To generate th…
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Handwritten Digit Recognition using Machine and Deep Learning Algorithms Open
The reliance of humans over machines has never been so high such that from\nobject classification in photographs to adding sound to silent movies\neverything can be performed with the help of deep learning and machine learning\nalgorithms.…
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Arabic Handwritten Digit Recognition Based on Restricted Boltzmann Machine and Convolutional Neural Networks Open
Handwritten digit recognition is an open problem in computer vision and pattern recognition, and solving this problem has elicited increasing interest. The main challenge of this problem is the design of an efficient method that can recogn…
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Handwritten digits recognition with decision tree classification: a machine learning approach Open
Handwritten digits recognition is an area of machine learning, in which a machine is trained to identify handwritten digits. One method of achieving this is with decision tree classification model. A decision tree classification is a machi…
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Unknown-Length Handwritten Numeral String Recognition Using Cascade of PCA-SVMNet Classifiers Open
Automatic recognition of handwritten digit string with unknown length has many potential real applications. The most challenging step in this problem is how to efficiently segment connected and/or overlapped digits exhibited in the input i…
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Deep Learning Autoencoder Approach for Handwritten Arabic Digits Recognition Open
This paper presents a new unsupervised learning approach with stacked autoencoder (SAE) for Arabic handwritten digits categorization. Recently, Arabic handwritten digits recognition has been an important area due to its applications in sev…
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An Ensemble of Simple Convolutional Neural Network Models for MNIST Digit Recognition Open
We report that a very high accuracy on the MNIST test set can be achieved by using simple convolutional neural network (CNN) models. We use three different models with 3x3, 5x5, and 7x7 kernel size in the convolution layers. Each model con…
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Hierarchical Convolutional Neural Network for Handwritten Digits Recognition Open
The application of a combination of convolutional neural networks for the recognition of handwritten digits is considered. Recognition is carried out by two sets of the networks following each other. The first neural network selects two di…
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Recognition of Handwritten Digit using Convolutional Neural Network (CNN) Open
Humans can see and visually sense the world around them by using their eyes and brains. Computer vision works on enabling computers to see and process images in the same way that human vision does. Several algorithms developed in the area …
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Convolutional ensembles for Arabic Handwritten Character and Digit Recognition Open
A learning algorithm is proposed for the task of Arabic Handwritten Character and Digit recognition. The architecture consists on an ensemble of different Convolutional Neural Networks. The proposed training algorithm uses a combination of…
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Assessing four Neural Networks on Handwritten Digit Recognition Dataset (MNIST) Open
Although the image recognition has been a research topic for many years, many researchers still have a keen interest in it[1]. In some papers[2][3][4], however, there is a tendency to compare models only on one or two datasets, either beca…
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MNIST-MIX: a multi-language handwritten digit recognition dataset Open
In this note, we contribute a multi-language handwritten digit recognition dataset named MNIST-MIX, which is the largest dataset of the same type in terms of both languages and data samples. With the same data format with MNIST, MNIST-MIX …
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Effective Handwritten Digit Recognition using Deep Convolution Neural Network Open
This paper proposed a simple neural network approach towards handwritten digit recognition using convolution.With machine learning algorithms like KNN,SVM/SOM, recognizing digits is considered as one of the unsolvable tasks due to its dist…
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Handwritten digit recognition by support vector machine optimized by Bat algorithm Open
Handwritten digit recognition is an important but very hard practical problem. This is a classification problem\nfor which support vector machines are very successfully used. Determining optimal support vector machine is\nanother hard opti…
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HDSR-Flor: A Robust End-to-End System to Solve the Handwritten Digit String Recognition Problem in Real Complex Scenarios Open
Automatic handwriting recognition systems are of interest for academic research fields and for commercial applications. Recent advances in deep learning techniques have shown dramatic improvement in relation to classic computer vision prob…
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An Efficient CNN Model for Automated Digital Handwritten Digit Classification Open
Background: Handwriting recognition becomes an appreciable research area because of its important practical applications, but varieties of writing patterns make automatic classification a challenging task. Classifying handwritten digits wi…
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A Minimal Spiking Neural Network to Rapidly Train and Classify Handwritten Digits in Binary and 10-Digit Tasks Open
This paper reports the results of experiments to develop a minimal neural network for pattern classification. The network uses biologically plausible neural and learning mechanisms and is applied to a subset of the MNIST dataset of handwri…
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Automatic CNN-Based Arabic Numeral Spotting and Handwritten Digit Recognition by Using Deep Transfer Learning in Ottoman Population Registers Open
Historical manuscripts and archival documentation are handwritten texts which are the backbone sources for historical inquiry. Recent developments in the digital humanities field and the need for extracting information from the historical …
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Improvement of MNIST Image Recognition Based on CNN Open
At present, great progress has been made in the field of image recognition, especially in convolutional neural network. Lenet-5 convolutional neural network has been able to identify handwritten digit MNIST database with high precision. In…
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Handwritten Digit Recognition Using Machine Learning Open
Technology is getting more and more involved in our lives, and so are algorithms. These algorithms speed up work and reduce workload. Especially machine learning algorithms are improving day by day by imitating human behaviours. Handwritin…
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HANDWRITTEN DIGIT RECOGNITION USING VARIOUS MACHINE LEARNING ALGORITHMS AND MODELS Open
Handwritten digit recognition is a technique or technology for automatically recognizing and detecting handwritten digital data through different Machine Learning models.In this paper we use various Machine Learning algorithms to enhance t…
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A Minimal Subset of Features Using Feature Selection for Handwritten Digit Recognition Open
Many systems of handwritten digit recognition built using the complete set of features in order to enhance the accuracy. However, these systems lagged in terms of time and memory. These two issues are very critical issues especially for re…
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Persian Handwritten Digit Recognition Using Combination of Convolutional Neural Network and Support Vector Machine Methods Open
Persian handwritten digit recognition is one of the important topics of image processing which significantly considered by researchers due to its many applications. The most important challenges in Persian handwritten digit recognition is …