Handwritten Digits Recognition using Machine and Deep Learning

Information processing has shown a great deal of use for handwritten digit recognition. However, because people's writing styles vary so much, correctly identifying these characters from photos is a challenging undertaking. Furthermore, this procedure is complicated by the presence of various visual effects such as noise, blurring, and intensity changes. In our proposed approach, we have address all these limitations by introducing a deep learning-based technique. The model is trained on these images in order to identify and classify the numerals from zero to nine. Our model showed an average accuracy value of 99.81%.The experimental findings demonstrate that, in spite of different writing styles such as noise, blurring and changes in number position and size, our technique is able to reliably classify Handwritten Digit Recognition from photographs. Additionally, the provided method can generalized effectively to scenarios that have not yet been seen, indicating that the model is a useful solution for numeral recognition.