Baaqar-007/dyslexia-accessibility-nlp

A multi-model ML pipeline that detects dyslexia indicators from handwriting images by combining a letter classifier, a CNN reversal detector, and an LSTM sequence analyser into a weighted ensemble. Built as a research capstone exploring how deep learning can assist early screening in under-resourced educational settings. # Dyslexia Accessibility NLP A multi-model deep learning pipeline for dyslexia screening from handwriting images. Three heterogeneous models — a scikit-learn MLP letter classifier, a PyTorch CNN reversal detector, and a PyTorch Bidirectional LSTM sequence anomaly detector — are fused via a clinically motivated weighted ensemble and served through a Flask web application with structured PDF reporting. This project is framed as a research capstone on multi-model fusion for social impact, not a production tool. Every architectural and mathematical decision is motivated and documented below. --- ## Project Structure ``` dyslexia-accessibility-nlp/ │ ├── beta versions/ ← Original iterative development history │ ├── Letter_Classification/ · Scratch MLP (NumPy/Numba), GridSearch, │ │ ├── scripts/ output predictions at 10k/30k/88.8k samples │ │ └── output/ │ ├── Dyslexic_Detection/ · TF/Keras CNN training notebook, │ │ └── testing_tf.ipynb checkpoint .h5 models │ ├── nlp_module.py · Original incomplete LSTM module │ └── README.md │ ├── data/ ← Data loading, preprocessing, augmentation │ ├── preprocessing.py · EMNIST loader (orientation fix), │ │ stratified splits, StandardScaler │ ├── augmentation.py · torchvision transforms + PyTorch DataLoader │ └── nlp_data_generator.py · Synthetic sequence generator with │ MLP confusion noise (domain adaptation) │ ├── models/ ← Model definitions and training scripts │ ├── mlp_classifier.py · 3-layer MLP (512→256→128), Adam, │ │ sklearn early stopping │ ├── cnn_classifier.py · 3-block Conv2D CNN, BatchNorm, Dropout, │ │ GlobalAvgPool, PyTorch AMP (GPU) │ ├── nlp_sequenc …