This CNN fine-tuned model is designed to identify Alphabet characters written by children for ages between 4-8.

This model focuses on adapting a pre-trained CNN model. It is built of three convolutional layers with 32 filters, followed by max-pooling and batch normalization. Another set of three convolutional layers with 64 filters extracts deeper features, followed by another pooling and normalization step. The extracted features are passed through two fully connected layers with dropout, and the final softmax layer classifies the characters into 28 categories.

The base model, trained on AHCD, and I've fine-tuned it on Dhad-Hijja Dataset collection,

Could be used by anyone who is interested in buliding App, Model, anything that should identify Children's characters handwriting.

Training Accuracy :86%

Test Accuracy: 87%

Try it out live here: