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The handwritten digit recognition problem becomes one of the most famous problems in machine learning and computer vision applications. Many machine learning techniques have been employed to solve the handwritten digit recognition problem. This paper focuses on Neural Network (NN) approaches. The most three famous NN approaches are deep neural network (DNN), deep belief network (DBN) and convolutional...
In a computer vision system, handwritten digits recognition is a complex task that is central to a variety of emerging applications. It has been widely used by machine learning and computer vision researchers for implementing practical applications like computerized bank check numbers reading. In this study, we implemented a multi-layer fully connected neural network with one hidden layer for handwritten...
Real-world data such as medical images and sensor measurements is usually high-dimensional and limited. Using such datasets directly in machine learning tasks can lead to poor generalization. Feature learning is a general approach for transforming high-dimensional data points to a representational space with lower dimensionality. Machine learning models can be trained efficiently with such representations...
Handwritten mathematical symbols and equations recognition has captured a lot of concentration in the field of pattern recognition. Using efficient multilayer perceptron feed forward back propagation neural network with training algorithm gradient descent with momentum and adaptive learning definitely improve the performance and accuracy of proposed system. By considering hybrid feature in recognition...
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.,...
Writer adaptation is an important topic in handwriting recognition, which can further improve the performance of writer-independent recognizer. In this paper, we propose combining the neural network classifier with style transfer mapping (STM) for unsupervised writer adaptation, which only require writer-specific unlabeled data, and therefore is more common and efficient compared to supervised adaptation...
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...
This paper proposes an Intelligent Handwriting Thai Signature Recognition System base on Multilayer Perceptron and Radial Basis Network. The proposed system compose of three main processes, i.e. image pre-processing, feature extraction and Thai signature recognition. In the recognition processes the neural network is used into two stage. First, Multilayer Perceptron (MLP) and Radial Basis Function...
Handwritten Bangla digit recognition is one of the most attractive area for researchers who have interest in image processing and pattern recognition field. In our everyday activities like bank check identification, passport and document analysis, number plate identification and especially in our postal automation service, recognition of handwritten digits plays a significant role. That's why a rich...
In this paper, a novel system for segmentation and recognition of handwritten Persian bank checks is presented. Our focus in this paper is on segmentation and recognition of handwritten courtesy amounts and dates of Persian checks. We present the results of our tests on different levels of check fields including: isolated digits, courtesy amounts, and dates. Courtesy amounts and dates used for experiments...
This paper briefly introduced the genetic algorithm (GA) and, BP algorithm (Back Propagation Algorithm), and proved the BP neural network of handwritten digital recognition performance is superior to the only use in the BP neural network of handwritten digital recognition on the basis of the practical application of handwritten digital recognition based on GA - BP.
This paper discusses signature verification and recognition using a new approach that depends on a neural network which enables the user to recognize whether a signature is original or a fraud. The user introduces into the computer the scanned images, modifies their quality by image enhancement and noise reduction techniques, to be followed by feature extraction and neural network training, and finally...
It has been 50 years since the idea popped up that calculating systems can be made on the replica of the biological neural networks. Still, the development of this science branch made the improvement of these systems possible only in the last 25-30 years. Nowadays, neural computing is a very extensive, separate science. Its solid theory basis made it possible to use them to solve many kind of problems...
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