📌Handwriting_Recognition_Image_Classification

Develop ML models for image classification, then convert those models into the TF-Lite file format, which can be embedded on Android and iOS. This dataset consists of more than four hundred thousand handwritten names collected through charity projects.

Dataset: Handwriting Recognition

🎯The following are the criteria for this project:

• This project has a train accuracy of 82% and a validation accuracy of 96%
• This dataset has 413_704 images
• The images in the dataset have non-uniform resolution
• Using a sequential model
• Using the Conv2D Maxpooling Layer
• Implement callbacks
• Make a plot against the accuracy and loss of the model
• Save models in TF-Lite format