This repository contains code for training and predicting A-Z handwritten alphabet images using deep learning models. It consists of two Google Colab notebooks: one for training the models and another for testing and predicting with the trained models.
Handwriting_Recognition_Models.ipynb: This notebook contains the code for training the A-Z handwritten alphabet recognition models. It covers the data preprocessing, model architecture definition, training process, and model evaluation.
Prediction_test.ipynb: This notebook is used for testing and predicting with the trained models. It includes code for loading the trained models, preprocessing the test images, and making predictions.
The dataset used for training the models is sourced from Kaggle and consists of handwritten alphabet images. The dataset contains images for each uppercase letter from A to Z.
To replicate the training process, you need to download the dataset from Kaggle and place it in the appropriate directory. this is the link to kaggle dataset Kaggle
Open Handwriting_Recognition_Models.ipynb in Google Colab and follow the instructions in the notebook to train the models. Make sure to set the correct file paths and adjust any hyperparameters if needed.
After training the models, open Prediction_test.ipynb in Google Colab to load the trained models and perform predictions on test images. Adjust the file paths as necessary.