Recognition of Handwritten Digit using Convolutional Neural Network (CNN (original) (raw)
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Handwriting Digit Recognition had gotten more interest in the field of pattern recognition and machine learning. Optical Character Recognition (OCR) and Handwritten Digit Recognition (HDR) each have specific domains in which they can be used for Digit Recognition in a HDR System. Some various strategies has been proposed, although there are enough research and papers that outline the ways for transferring textual content from a paper document to machine-readable form. Digit Recognition systems may play a crucial role in creating a paperless world in future by digital conversion and processing the remaining paper documents. This paper provides all-inclusive overview of HDR. Deep learning has recently taken a radical turn in the field of machine learning by making it more artificially intelligent, thanks to the rise of Artificial Neural Networks (ANN). Because of the widely extended range of applications, deep learning is employed in a broad range of industries, including surveillance, health, medicine, sports, robots, and drones. Convolutional Neural Network (CNN) is the core of astonishing developments in deep learning, combining ANN and cuttingedge deep learning algorithms. Pattern recognition, phrase classification, audio recognition, face recognition, text classification, document analysis, a scene recognition, and HDR are one of the few applications. The purpose of this research is to differentiate the accuracies of the CNN in classifying handwritten digits by using varying numbers of hidden layers and epochs. We experimented to evaluate CNN's performance.
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MNIST, Modified National Institute of Standards and Technology, is the largest database of handwritten numbers used in deep learning, and machine learning. In this project, a hands-on experience of applying machine learning and pattern recognition techniques is given to a real-world data set such as MNIST. Multiple building blocks have been proposed and analyzed to improve the speed and the accuracy of the Convolutional Neural Networks (CNN). Two networks have been used with the same data. In network I, a three-layer MLP with ReLU and dropout resulting in fast training process with over all accuracy 95% during training and 94% for testing. Network II on the other hand, a stack of CNN, RelU, and Max pooling shows slower training process with better accuracy than network I and overall, 99% accuracy for training, and 98.9% for testing. Another modification on network II improved overall accuracy during the training to 99.82% and accuracy for testing to 99.25%. this modification will be shown in the report. The building blocks for the project will be discussed briefly with the results and figures. Python code is also provided for this project. This project may be used for as a guidance for new students or engineers who aiming to understand pattern recognition.
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The recognition of handwritten digits has aroused the interest of the scientific community and is the subject of a large number of research works thanks to its various applications. The objective of this paper is to develop a system capable of recognizing handwritten digits using a convolutional neural network (CNN) combined with machine learning approaches to ensure diversity in automatic classification tools. In this work, we propose a classification method based on deep learning, in particular the convolutional neural network for feature extraction, it is a powerful tool that has had great success in image classification, followed by the support vector machine (SVM) for higher performance. We used the dataset (MNIST), and the results obtained showed that the combination of CNN with SVM improves the performance of the model as well as the classification accuracy with a rate of 99.12%.
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International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2023
For many years, numerous methods have been used in extensive research on handwriting recognition. The capacity to create an effective algorithm that can recognise handwritten digits given by users via scanner, tablet, and other digital devices is at the core of the issue. The automatic processing of bank checks, postal addresses, and other sorts of data already makes substantial use of handwritten digit recognition. Computational intelligence methods like artificial neural networks used by several current systems. CNN and the MNIST data set will be used to complete this. Handwriting Recognition, Deep Learning, CNN, and Computational Intelligence are key terms. I. INTRODUCTION Handwriting Recognition is a machine's ability to recognise and predict human handwritten digits. It is a difficult task for machines because handwritten digits are not perfect and can be made in a variety of flavours. As a result, this paper presents a solution for accurately recognising and predicting handwritten digits. The number recognition framework is simply a task that the machine must complete in order to prepare and interpret numbers. Handwritten digit recognition interprets manually written numbers from a variety of sources such as messages, bank checks, documents, photos, and so on, as well as in a variety of situations for web-based handwriting recognition on PC Tablets and in vehicles. There are numerous techniques that can be used to gain recognition. Convolutional Neural Network (CNN), Semi Incremental Method,Line and Word Segmentation, and other techniques are used. Convolutional Neural Networks are one of the most effective and well-known methods of handwriting recognition (CNN). It's a component of deep learning. Artificial neurons make up Convolutional Neural Networks (CNN). The number recognition model recognises numbers from various sources using large datasets.