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The dataset used is **[Standard OCR Dataset](https://www.kaggle.com/datasets/preatcher/standard-ocr-dataset/)**.\n\n---\n\n## ⚙️ Libraries\n- TensorFlow / Keras  \n- NumPy  \n- Matplotlib  \n- scikit-learn  \n\n---\n\n## 🏗️ Model Architecture\nThe model is based on a simple **CNN**, including:\n- Conv2D + MaxPooling layers (feature extraction)  \n- Flatten + Dense layers (classification)  \n- Softmax output with **36 classes (0–9, A–Z)**  \n\n---\n\n## 🚀 Results\n- Training accuracy: **~97%**  \n- Validation accuracy: **~95%**  \n- Test accuracy: **~94%**  \n\nThe model performs well in recognizing handwritten letters and digits, with minor misclassifications in similar-looking characters (e.g., `O` vs `0`).  \n\n---\n\n## 📌 Notes\n- Input images are **grayscale** with size **64x64**.  \n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frifaamrilsahputra%2Focr-alphanumeric-cnn","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frifaamrilsahputra%2Focr-alphanumeric-cnn","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frifaamrilsahputra%2Focr-alphanumeric-cnn/lists"}