Refactor README.md for clarity and quick start instructions for OCR scripts

README.md

@@ -3,100 +3,68 @@ viewer: false
 tags: [uv-script, ocr, vision-language-model, document-processing]
 ---
 
-# UV Scripts - OCR Collection
+# OCR UV Scripts
 
-This repository contains UV scripts for OCR (Optical Character Recognition) tasks using various models.
+Ready-to-run OCR scripts that work with `uv run` - no setup required!
 
-## 🚧 Early Testing Version
+## 🚀 Quick Start with HuggingFace Jobs
 
-This is an early version for testing. Documentation and examples will be expanded based on feedback.
-
-## Available Scripts
-
-### 1. Nanonets OCR (`nanonets-ocr.py`)
-
-Converts document images to structured markdown using the Nanonets-OCR-s model.
-
-**Features:**
-- LaTeX equation recognition
-- Table extraction and formatting
-- Document structure preservation
-- Batch processing with vLLM
-
-**Requirements:**
-- GPU with CUDA support
-- Python 3.11+
-
-## Quick Test
-
-To test the script with a sample dataset:
+Run OCR on any dataset without a GPU:
 
 ```bash
-# Test with 5 samples from a document dataset
-uv run nanonets-ocr.py \
-    davanstrien/scientific-papers-small \
-    my-test-ocr-output \
-    --max-samples 5
-
-# Or if you have a specific dataset with images
-uv run nanonets-ocr.py \
-    your-username/your-image-dataset \
-    your-username/test-ocr-results \
-    --image-column image \
-    --max-samples 10
+hf jobs uv run --flavor l4x1 \
+    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/nanonets-ocr.py \
+    your-input-dataset your-output-dataset
 ```
 
-## Example Output
+That's it! The script will:
 
-The script adds a `markdown` column to your dataset containing the extracted text in markdown format, preserving:
-- Headers and document structure
-- Tables with proper formatting
-- Mathematical equations in LaTeX
-- Lists and other formatting
+- ✅ Process all images in your dataset
+- ✅ Add OCR results as a new `markdown` column
+- ✅ Push the results to a new dataset
 
-## GPU Memory
+## 📋 Available Scripts
 
-If you encounter GPU memory issues, adjust the batch size and memory utilization:
+### Nanonets OCR (`nanonets-ocr.py`)
 
-```bash
-uv run nanonets-ocr.py input output \
-    --batch-size 4 \
-    --gpu-memory-utilization 0.5
-```
+State-of-the-art document OCR using [nanonets/Nanonets-OCR-s](https://huggingface.co/nanonets/Nanonets-OCR-s) that handles:
 
-## Running on HuggingFace Jobs
+- 📐 **LaTeX equations** - Mathematical formulas preserved
+- 📊 **Tables** - Extracted as HTML format
+- 📝 **Document structure** - Headers, lists, formatting maintained
+- 🖼️ **Images** - Captions and descriptions included
+- ☑️ **Forms** - Checkboxes rendered as ☐/☑
 
-Run this script on HF infrastructure without needing your own GPU!
+## 💻 Usage Examples
 
-### Command Line
+### Run on HuggingFace Jobs (Recommended)
+
+No GPU? No problem! Run on HF infrastructure:
 
 ```bash
-# Basic usage
+# Basic OCR job
 hf jobs uv run --flavor l4x1 \
     https://huggingface.co/datasets/uv-scripts/ocr/raw/main/nanonets-ocr.py \
-    input-dataset-id output-dataset-id
+    your-input-dataset your-output-dataset
 
-# Full example with options
-hf jobs uv run --flavor l4x1 \
+# Real example with UFO dataset 🛸
+hf jobs uv run \
+    --flavor a10g-large \
+    --image vllm/vllm-openai:latest \
+    -e HF_TOKEN=$(python3 -c "from huggingface_hub import get_token; print(get_token())") \
     https://huggingface.co/datasets/uv-scripts/ocr/raw/main/nanonets-ocr.py \
-    NationalLibraryOfScotland/Scottish-School-Exam-Papers \
-    your-username/scottish-exams-ocr \
+    davanstrien/ufo-ColPali \
+    your-username/ufo-ocr \
     --image-column image \
     --max-model-len 16384 \
-    --batch-size 16
+    --batch-size 64
 
-# With HF token for private repos
-hf jobs uv run --flavor l4x1 --secret HF_TOKEN=$HF_TOKEN \
+# Private dataset with custom settings
+hf jobs uv run --flavor l40sx1 \
+    -e HF_TOKEN=$(python3 -c "from huggingface_hub import get_token; print(get_token())") \
     https://huggingface.co/datasets/uv-scripts/ocr/raw/main/nanonets-ocr.py \
-    input-dataset output-dataset \
-    --private
-
-# With vLLM Docker image for optimized performance
-hf jobs uv run \
-    --flavor l4x1 \
-    --image vllm/vllm-openai:latest \
-    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/nanonets-ocr.py \
-    input-dataset output-dataset \
+    private-input private-output \
+    --private \
     --batch-size 32
 ```
 
@@ -105,30 +73,37 @@ hf jobs uv run \
 ```python
 from huggingface_hub import run_uv_job
 
-# Run the OCR script
 job = run_uv_job(
     "https://huggingface.co/datasets/uv-scripts/ocr/raw/main/nanonets-ocr.py",
-    args=[
-        "input-dataset-id",
-        "output-dataset-id", 
-        "--image-column", "image",
-        "--max-model-len", "16384"
-    ],
-    flavor="l4x1",
-    secrets={"HF_TOKEN": "your-token"}  # if needed
+    args=["input-dataset", "output-dataset", "--batch-size", "16"],
+    flavor="l4x1"
 )
 ```
 
-### Recommended GPU Flavors
+### Run Locally (Requires GPU)
+
+```bash
+# Clone and run
+git clone https://huggingface.co/datasets/uv-scripts/ocr
+cd ocr
+uv run nanonets-ocr.py input-dataset output-dataset
+
+# Or run directly from URL
+uv run https://huggingface.co/datasets/uv-scripts/ocr/raw/main/nanonets-ocr.py \
+    input-dataset output-dataset
+```
 
-- **`l4x1`** (24GB) - Recommended for most OCR tasks
-- **`t4-small`** (16GB) - For smaller batches or lower resolution
-- **`a10g-small`** (24GB) - Alternative to L4
-- **`l40sx1`** (48GB) - For very large batches
-- **`a100-large`** (80GB) - Maximum performance
+## 🎛️ Configuration Options
 
-## Coming Soon
+| Option                     | Default | Description                 |
+| -------------------------- | ------- | --------------------------- |
+| `--image-column`           | `image` | Column containing images    |
+| `--batch-size`             | `8`     | Images processed together   |
+| `--max-model-len`          | `8192`  | Max context length          |
+| `--max-tokens`             | `4096`  | Max output tokens           |
+| `--gpu-memory-utilization` | `0.7`   | GPU memory usage            |
+| `--split`                  | `train` | Dataset split to process    |
+| `--max-samples`            | None    | Limit samples (for testing) |
+| `--private`                | False   | Make output dataset private |
 
-- Additional OCR models (RolmOCR, OlmOCR)
-- Performance benchmarks
-- More examples and use cases
+More OCR VLM Scripts coming soon! Stay tuned for updates!