EMNIST Dataset

Download the EMNIST ByClass dataset.

Preprocess the images to enhance contrast and remove noise.

Train a convolutional neural network (CNN) on the preprocessed images.

Evaluate the model's performance on a validation set of historical documents.

Fine-tune the model to improve accuracy on specific fonts and writing styles.

Download the EMNIST Letters dataset.

Train a recurrent neural network (RNN) on the dataset to capture sequential dependencies in handwriting.

Integrate the trained model into a mobile app.

Implement a user interface for handwriting input.

Optimize the model for low latency and memory usage on mobile devices.

Download the EMNIST Digits and EMNIST Letters datasets.

Train a hybrid model combining CNN and RNN to recognize both digits and letters.

Implement an algorithm to extract text from scanned exam papers.

Apply the trained model to recognize handwritten answers.

Develop a grading system based on the recognized text.

Download the EMNIST ByMerge dataset.

Train a generative adversarial network (GAN) to generate synthetic CAPTCHA images.

Train a CNN on the real and synthetic CAPTCHA images.

Evaluate the model's performance against various CAPTCHA breaking techniques.

Continuously update the model with new training data to maintain its robustness.

Download all six EMNIST splits.

Experiment with different machine learning architectures and training techniques.

Evaluate the performance of the new algorithms on the EMNIST datasets.

Compare the results with state-of-the-art methods.

Publish the findings in scientific journals and conferences.