• Tadikonda Likhitha
• Manoj V S Maddula
• Yash Singh Shekhawath
• Deepika Priya Kuntilla

Handwritten digit recognition is a complex task and requires a robust model which can identify digits from different sources of data. To achieve this, a CNN based approach can be adopted. CNN is a type of deep learning model which can learn from raw data and does not require any feature engineering. CNN consists of convolutional layers which perform convolutions on the data and extract features from it. MNIST is a dataset of handwritten digits which can be used to train and evaluate the model. The main objective of this research project is to develop a model using CNN, which can accurately recognize handwritten digits from the MNIST dataset. This model will be developed using Python programming language and Keras framework. The model will be trained using the MNIST dataset and evaluated using different metrics such as accuracy, precision and recall. The model will be further tested on various handwritten digits to evaluate its performance. This research will also involve experiments and analysis to gain insights into the behavior of the model. The experiments will involve changing the hyperparameters of the CNN such as the number of convolutional layers, filter size, number of neurons and other model parameters. The results from the experiments will be analyzed to determine the best parameters for the model. Additionally, the model will be tested on other datasets to evaluate its generalization ability. The results of this research project will be used to develop a robust model for handwritten digit recognition which can accurately classify digits from different sources of data. The model and the insights gained from the experiments and analysis will be useful for further research and development in the field of digit recognition.

"Handwritten Digit Recognition using PCA , CNN and NumPy", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 3, page no.g413-g420, March-2023, Available :http://www.jetir.org/papers/JETIR2303661.pdf

"Handwritten Digit Recognition using PCA , CNN and NumPy", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 3, page no. ppg413-g420, March-2023, Available at : http://www.jetir.org/papers/JETIR2303661.pdf