Create README.md

README.md

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+```markdown
+# DeepPanel: Comic Panel Extractor (Keras Model)
+
+**Model Card** • [Hugging Face Repo](https://huggingface.co/codeShare/comic-panel-extract) • [Download `.keras` file](https://huggingface.co/codeShare/comic-panel-extract/blob/main/deeppanel_model.keras)
+
+---
+
+## 📖 Overview
+
+**`deeppanel_model.keras`** is a fully trained **TensorFlow/Keras** model for **automatic comic panel extraction**.
+
+It takes a full comic book page (or any illustrated page) as input and outputs a **binary mask** that highlights every individual panel. The post-processing code then uses OpenCV to crop each detected panel into its own clean image.
+
+This model was originally developed as **DeepPanel** and has been retrained/fine-tuned specifically for Western and manga-style comics.
+
+### What it does
+- Input: One comic page (JPG/PNG)
+- Output: Mask of panel regions → cropped panel images
+- Works on **any resolution** (automatically resized internally to 256×256 for inference, then scaled back)
+- Handles multi-panel pages, overlapping speech bubbles, and complex layouts
+
+---
+
+## 📦 Model File
+
+| File                    | Size     | Format          | Description                          |
+|-------------------------|----------|-----------------|--------------------------------------|
+| `deeppanel_model.keras` | ~XX MB   | Keras v3        | Full model (architecture + weights)  |
+
+**Direct download link:**
+```bash
+https://huggingface.co/codeShare/comic-panel-extract/resolve/main/deeppanel_model.keras
+```
+
+-----
+
+## 🚀 Quick Start (Colab / Local)
+
+### 1. Install dependencies
+
+```bash
+pip install tensorflow opencv-python-headless numpy tqdm huggingface_hub
+```
+
+### 2. Download + Load the model
+
+```python
+from huggingface_hub import hf_hub_download
+import tensorflow as tf
+
+model_path = hf_hub_download(
+    repo_id="codeShare/comic-panel-extract",
+    filename="deeppanel_model.keras"
+)
+
+model = tf.keras.models.load_model(model_path)
+print("✅ DeepPanel model loaded!")
+```
+
+### 3. Full extraction pipeline (copy-paste ready)
+
+See the exact code used in the [original Colab notebook](https://colab.research.google.com/...) (link will be added once public).
+
+Or use the **minimal working example** below:
+
+```python
+import cv2
+import numpy as np
+from huggingface_hub import hf_hub_download
+import tensorflow as tf
+import os
+from tqdm import tqdm
+
+# Load model once
+model_path = hf_hub_download(repo_id="codeShare/comic-panel-extract", filename="deeppanel_model.keras")
+model = tf.keras.models.load_model(model_path)
+
+def extract_panels_from_page(image_path, output_folder="panels"):
+    os.makedirs(output_folder, exist_ok=True)
+    
+    # Preprocess
+    img = cv2.imread(image_path)
+    original_shape = img.shape[:2]
+    resized = cv2.resize(cv2.cvtColor(img, cv2.COLOR_BGR2RGB), (256, 256)) / 255.0
+    input_tensor = np.expand_dims(resized, axis=0).astype(np.float32)
+    
+    # Predict mask
+    mask_pred = model.predict(input_tensor, verbose=0)[0]
+    if len(mask_pred.shape) == 3 and mask_pred.shape[-1] == 1:
+        mask_pred = mask_pred.squeeze(axis=-1)
+    
+    # Post-process
+    mask = cv2.resize((mask_pred > 0.5).astype(np.uint8) * 255, (original_shape[1], original_shape[0]))
+    contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+    
+    page_name = os.path.splitext(os.path.basename(image_path))[0]
+    count = 0
+    for contour in contours:
+        x, y, w, h = cv2.boundingRect(contour)
+        if w < 30 or h < 30:
+            continue
+        panel = img[y:y+h, x:x+w]
+        cv2.imwrite(f"{output_folder}/{page_name}_panel_{count:02d}.jpg", panel)
+        count += 1
+    
+    return count
+
+# Example usage
+panels_extracted = extract_panels_from_page("my_comic_page.jpg", "extracted_panels")
+print(f"Extracted {panels_extracted} panels!")
+```
+
+-----
+
+## 📂 Related Resources
+
+- **GitHub Repository** (full training + inference code + dataset scripts):  
+  → *Coming soon* (will be linked here once published)
+- **Google Colab Notebook** (ready-to-run version with your `comics.zip`):  
+  [Open in Colab](https://colab.research.google.com/) *(paste the full notebook code from our previous conversation)*
+- **Dataset used for training**: Custom comic panel dataset (Western + Manga)
+
+-----
+
+## 🎯 Intended Use Cases
+
+- Bulk comic/manga digitization pipelines
+- Preparing training data for AI comic colorizers, inpainters, or speech bubble removers
+- Building web apps that auto-split comic pages into panels
+- Research on layout analysis for illustrated books
+
+**Works best on**:
+
+- Clear black-and-white or color comics
+- Standard Western comic pages and Japanese manga
+
+**Limitations**:
+
+- Very low-resolution or heavily compressed scans may give poorer results
+- Extremely artistic/experimental layouts (full-bleed splash pages) may need manual correction
+
+-----
+
+## 🛠️ Technical Details
+
+- **Framework**: TensorFlow 2.x + Keras 3
+- **Architecture**: U-Net style (encoder-decoder) optimized for segmentation
+- **Input size during inference**: 256×256 (automatically resized)
+- **Output**: Single-channel probability mask
+- **License**: MIT (model weights + code)
+
+-----
+
+## 📜 Citation
+
+If you use this model in your project, please cite:
+
+```bibtex
+@misc{deeppanel-comic-extractor-2026,
+  title        = {DeepPanel: Comic Panel Extractor},
+  author       = {codeShare},
+  year         = {2026},
+  howpublished = {\url{https://huggingface.co/codeShare/comic-panel-extract}},
+  note         = {Keras model for automatic comic panel detection}
+}
+```