Remove dots-ocr from README due to compatibility issues

The script remains in the repository but is removed from documentation
until compatibility issues are resolved.

README.md

@@ -50,15 +50,6 @@ State-of-the-art document OCR using [nanonets/Nanonets-OCR-s](https://huggingfac
 - 🖼️ **Images** - Captions and descriptions included
 - ☑️ **Forms** - Checkboxes rendered as ☐/☑
 
-### dots.ocr (`dots-ocr.py`)
-
-Advanced document layout analysis and OCR using [rednote-hilab/dots.ocr](https://huggingface.co/rednote-hilab/dots.ocr) that provides:
-
-- 🎯 **Layout detection** - Bounding boxes for all document elements
-- 📑 **Category classification** - Text, Title, Table, Formula, Picture, etc.
-- 📖 **Reading order** - Preserves natural reading flow
-- 🌍 **Multilingual support** - Handles multiple languages seamlessly
-- 🔧 **Flexible output** - JSON, structured columns, or markdown
 
 ## 🆕 New Features
 
@@ -109,13 +100,6 @@ hf jobs uv run --flavor l4x1 \
     https://huggingface.co/datasets/uv-scripts/ocr/raw/main/nanonets-ocr.py \
     your-input-dataset your-output-dataset
 
-# Document layout analysis with dots.ocr
-hf jobs uv run --flavor l4x1 \
-    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/dots-ocr.py \
-    your-input-dataset your-layout-dataset \
-    --mode layout-all \
-    --output-format structured \
-    --use-transformers  # More compatible backend
 
 # Real example with UFO dataset 🛸
 hf jobs uv run \
@@ -166,10 +150,6 @@ uv run https://huggingface.co/datasets/uv-scripts/ocr/raw/main/nanonets-ocr.py \
 uv run rolm-ocr.py documents extracted-text
 uv run rolm-ocr.py images texts --shuffle --max-samples 100  # Random sample
 
-# dots.ocr examples
-uv run dots-ocr.py documents analyzed-docs  # Full layout + OCR
-uv run dots-ocr.py scans layouts --mode layout-only  # Layout only
-uv run dots-ocr.py papers markdown --output-format markdown  # As markdown
 ```
 
 ## 📁 Works With
@@ -184,8 +164,8 @@ Any HuggingFace dataset containing images - documents, forms, receipts, books, h
 | -------------------------- | ------- | ----------------------------- |
 | `--image-column`           | `image` | Column containing images      |
 | `--batch-size`             | `32`/`16`* | Images processed together     |
-| `--max-model-len`          | `8192`/`16384`**/`24000`*** | Max context length     |
-| `--max-tokens`             | `4096`/`8192`**/`16384`*** | Max output tokens      |
+| `--max-model-len`          | `8192`/`16384`** | Max context length     |
+| `--max-tokens`             | `4096`/`8192`** | Max output tokens      |
 | `--gpu-memory-utilization` | `0.8`   | GPU memory usage (0.0-1.0)    |
 | `--split`                  | `train` | Dataset split to process      |
 | `--max-samples`            | None    | Limit samples (for testing)   |
@@ -195,29 +175,12 @@ Any HuggingFace dataset containing images - documents, forms, receipts, books, h
 
 *RolmOCR uses batch size 16
 **RolmOCR uses 16384/8192
-***dots.ocr uses 24000/16384
 
 ### RolmOCR Specific
 
 - Output column is auto-generated from model name (e.g., `rolmocr_text`)
 - Use `--output-column` to override the default name
 
-### dots.ocr Specific Options
-
-| Option              | Default | Description                           |
-| ------------------- | ------- | ------------------------------------- |
-| `--mode`            | `layout-all` | Processing mode: `layout-all`, `layout-only`, `ocr`, `grounding-ocr` |
-| `--output-format`   | `json` | Output format: `json`, `structured`, `markdown` |
-| `--filter-category` | None   | Filter by layout category (e.g., `Table`, `Formula`) |
-| `--output-column`   | `dots_ocr_output` | Column name for JSON output |
-| `--bbox-column`     | `layout_bboxes` | Column for bounding boxes (structured mode) |
-| `--category-column` | `layout_categories` | Column for categories (structured mode) |
-| `--text-column`     | `layout_texts` | Column for texts (structured mode) |
-| `--markdown-column` | `markdown` | Column for markdown output |
-| `--use-transformers`| `False` | Use transformers backend instead of vLLM (more compatible) |
-
 💡 **Performance tip**: Increase batch size for faster processing (e.g., `--batch-size 128` for A10G GPUs)
 
-⚠️ **dots.ocr Note**: If you encounter vLLM initialization errors, use `--use-transformers` for a more compatible (but slower) backend.
-
 More OCR VLM Scripts coming soon! Stay tuned for updates!