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📌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