Devanagri Character Image Recognition and Conversion into Text using Long Short Term Memory

The goal of this project effort is to identify handwritten characters. Devanagri Lipi still struggles to accurately and simply recognise human handwriting. The discipline of deep learning has extensively studied the recognition of handwritten characters. Character recognition software is crucial for everyday tasks like interpreting bank checks and postal information. The difficulty in creating machine-readable characters is still greatly hampered by several different handwriting approaches and the inferior quality of handwriting when compared to printed text. Neural networks operate similarly to the human brain; algorithms spot patterns and address universal issues. Convolution neural networks receive varied input and use filters to learn from multiple visual properties. Our deep learning model, which will operate on a grid data format and include convolution and pooling layers, will be developed. Later, we will concentrate on developing a GUI where we can write characters for automatic character recognition.