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Classification of Handwritten Digits Using Hopfield Network Team Members Moyosore Alex Balogun, Megha Dubey, Vidisha Indeewari Liyana Arachchilage, Ashim Neupane

Table of Content • Purpose of the Project • Background • Methodology and Design • The Overarching Question • Summary • Conclusions • References • Group’s Short-bio

Purpose of the Project This project mainly focuses on developing a system capable of accurately recognizing and classifying handwritten digits using the Hopfield network as a novel way to mitigate various challenges when using the existing methods in the image classification field. This project explores how well the Hopfield network can handle the complexities of handwritten data compared to existing models by demonstrating how well the Hopfield network associative memory property can identify partially or incompletely presented data.

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Background Rapid technological advancement has increased the demand for robust and efficient pattern recognition and classification methods, mainly handwritten digit recognition (LeCun et al. , 1998). Handwritten digit recognition is a fundamental problem with applications ranging from postal automation to digitized document processing. Traditional approaches to digit recognition often rely on feature extraction and statistical methods. However, recent advancements in neural network architectures have opened up new possibilities for more accurate and robust classification. Inspired by the human brain, the Hopfield network has gained attention for its ability to store and recall patterns, making it a suitable candidate for handwritten digit classification. The Hopfield network, initially proposed by Johns Hopfield in 1982, is a type of recurrent neural network that exhibits associative memory properties (Hopfield, 1982).

Methodology and Design Research Design: This study looks at a Hopfield Network's ability to categorize handwritten numbers. An artificial neural network called the Hopfield Network is designed to tackle challenging categorization jobs. The model will be trained and tested using written digits from a library. Methodology: A machine learning strategy that is semi-supervised will be the technique used. It will first be necessary to prepare the training data and extract its characteristics. A recurrent neural network will then be trained using the features. The testing data will then be categorized using the trained model, and the outcomes will be contrasted with the ground truth. Ultimately, accuracy, recall, and precision will be used to gauge the model's performance.

The overarching Question How accurate is the Hopfield network classification of handwritten digits using the MNIST dataset? - What will be the model’s performance when colored images of the handwritten digits are presented other than gray scale images? - How will the model’s performance be impacted, when digitized images of partially completed/unclear handwritten characters are presented? - Would the Hopfield network show enough robustness to noise? - How would the changes to hyperparameters impact classification accuracy of the model?

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Summary In our project classification of handwritten digits using the Hopfield network, where we discussed both the Hebbian learning rule and Storkey's algorithm for training, it involves summarizing the essential findings and discussing the approach's effectiveness. In this project, we explored the application of the Hopfield network for the classification of handwritten digits, regarding the Hebbian learning rule and Storkey's algorithm for network training (Ritik, Rishika & Samay, 2021). Our findings demonstrate that the Hopfield network offers a robust approach to digit recognition when combined with these learning rules. The Hebbian learning rule reference provided us with a solid foundation for pattern association and memory recall, which are crucial in recognizing handwritten digits. In contrast, Storkey's algorithm enhanced the network's capability by improving its storage capacity. These two learning rules help to improve accuracy and efficiency in digit classification compared to traditional methods.The classification of handwritten digits using the Hopfield network, with the application of RNN methodologies, has shown promising results. The combination of Hopfield networks with RNN methodologies, particularly in the context of the MNIST database, showcases the evolution and effectiveness of machine learning techniques in pattern recognition and digit classification. The integration of advanced learning methods like the Storkey learning method and the application of deep learning algorithms, including RNNs, highlight the multi-faceted approach toward solving complex classification problems in handwritten digit recognition (Alonso et al., 2013).

Conclusions The project underlines the potential of combining Hopfield networks with Recurrent Neural Networks for complex pattern recognition tasks, particularly in handwritten digit classification. This synergy offers a promising avenue for future research and practical applications in neural networks and artificial intelligence. (Bernhard, 2021).Our project also highlighted the strengths and limitations of using Hopfield networks in pattern recognition. Challenges like network capacity and sensitivity to input variations were observed. Our future analysis experimenting with larger datasets and more complex digit styles could be beneficial in assessing the scalability and versatility of the approach (Sammedapatil,2019). Overall, this project contributes to understanding neural network-based handwritten digit classification. It highlights the potential of combining classical neural network models with innovative learning rules and paves the way for more advanced and efficient pattern recognition systems in the future. For future enhancement of our project, we can investigate methods to improve the efficiency of Hopfield networks in digit classification. This could involve optimizing network parameters, exploring different neuron models, or introducing new learning rules. Apart from that, we can assess the robustness of Hopfield networks to various types of noise in the data and explore the scalability of Hopfield networks for larger and more diverse datasets.

References LeCun et al.(1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324. https://ieeexplore.ieee.org/document/726791 Hopfield, J. J. (1982). Neural networks and physical systems with emergent collective computational abilities. Proceedings of the national academy of sciences, 79(8), 2554-2558. https://www.pnas.org/doi/10.1073/pnas.79.8.2554 Ritik, Rishika, Samay. (2021). Feature Extraction and Image Recognition of Cursive Handwritten English Words Using Neural Network and IAM Off‐Line Database. Smart and Sustainable Intelligent Systems , 91-102. Alonso-Weber, J. M., Sesmero, M. P., Gutierrez, G., Ledezma, A., & Sanchis, A. (2013). Handwritten digit recognition with pattern transformations and neural network averaging. In Artificial Neural Networks and Machine Learning–ICANN 2013: 23rd International Conference on Artificial Neural Networks Sofia, Bulgaria, September 10-13, 2013. Proceedings 23 (pp. 335-342). Springer Berlin Heidelberg. Sammedapatil, (2019). Handwritten Digit Recognition using Machine Learning https://medium.com/@sammedapatil003/handwritten-digit-recognition-2fa1ae839305

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Certificate of Authorship Certification of Authorship Submitted to : Dr. Tommy Blanchard Course : MSDS-690 Student’s Name: Moyosore Alex Balogun, Megha Dubey, Vidisha Indeewari Liyana Arachchilage, Ashim Neupane Date of Submission: 12/10/2023 Purpose and Title of Submission: Capstone Project Certification of Authorship : The team hereby certifies authorship of this document and that any assistance received in its preparation is fully acknowledged and disclosed in the document. The team has also cited all sources from which it obtained data, ideas, or words that are copied directly or paraphrased in the document. Sources are properly credited according to accepted standards for professional publications. The team also certifies that this paper was prepared by (group 4) for this purpose. Student’s Signature: Moyosore Alex Balogun, Megha Dubey, Vidisha Indeewari Liyana Arachchilage, Ashim Neupane

Group’s Short-bio Moyosore Alex Balogun: Moyosore has a background in supply chain and operations management. He is an aspiring Data Scientist, he likes to read, watch movies and play video games. Megha Dubey: Megha has knowledge and work experience on computer science fields especially on telecom and insurance sector. Apart from that, she like to explore and travel to new places, watching movies and cooking.

Deliverable 3_Team 4

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