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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...
The main aim of this paper is to implement a Artificial Neural Network to recognize and predict Handwritten digits from 0 to 9. A dataset comprises 5000 samples of number digits with different strokes are taken for our work. The dataset was trained using gradient descent Back-propagation algorithm and further tested using the Feed-forward algorithm. The system performance is observed by varying regularization...
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...
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.,...
Faced with the continuously increasing scale of data and expectation on response time, complex deep learning technologies, though highly accurate, present two non-rival challenges: a large amount of training data makes a model impossible to be built in short time and intolerable time-cost prohibits acceptable real-time responses. In this research we focus on improving the accuracy and efficiency of...
In this paper, we elaborate the advantages of combining two neural network methodologies, convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent neural networks, with the framework of hybrid hidden Markov models (HMM) for recognizing offline handwriting text. CNNs employ shift-invariant filters to generate discriminative features within neural networks. We show that CNNs are...
The finite impulse response multilayer perceptron (FIRMLP), a class of temporal processing neural networks, is a multilayer perceptron where the static weights (synapses) have been replaced by finite impulse response filters. Thus FIRMLPs are a type of convolutional neural network and different synapse types can be considered. We compare the performance of different network configurations for the...
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...
With the development of technology, Human-Computer Interface (HCI) system is playing a more and more important role in our daily life. HCI is a way to set up connections and to transfer information between human and computer. Pattern recognition based on Surface Electromyography (SEMG) is one of the most important HCI technologies. To make the input device of electronic products more portable to satisfy...
Automatic handwriting recognition of digit strings, is of academic and commercial interest. Current algorithms are already quite good at learning to recognize handwritten digits, which enables to use them for sorting letters and reading personal checks. Neural networks are a powerful technology for classification of visual inputs arising from documents, and have been extensively used in many fields...
Rising admissions in the South African institutions of higher education have enlarged student-to-lecturer ratios and increased the lecturer's workload, already burdened by administrative tasks. After marking tests, lecturers usually fill in a document called the cover page where the student's number, name and marks according to the questions are placed. Once this is done, they will have to recopy...
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...
This paper aims that analysing neural network method in pattern recognition. A neural network is a processing device, whose design was inspired by the design and functioning of human brain and their components. The proposed solutions focus on applying Bidirectional Associative Memory method model for pattern recognition. The primary function of which is to retrieve in a pattern stored in memory, when...
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.
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