Initial dots.ocr deployment for HuggingFace Space

README.md

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+---
+title: Dots OCR
+emoji: 🔍
+colorFrom: blue
+colorTo: green
+sdk: gradio
+sdk_version: 4.44.0
+app_file: app.py
+pinned: false
+license: apache-2.0
+models:
+  - rednote-hilab/dots.ocr
+---
+
+# dots.ocr - Multilingual OCR
+
+State-of-the-art multilingual document OCR supporting 100+ languages, tables, formulas, and complex layouts.

app.py

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+import gradio as gr
+from transformers import AutoModel, AutoTokenizer
+from PIL import Image
+import torch
+
+# Load model and tokenizer
+model_name = "rednote-hilab/dots.ocr"
+print("Loading dots.ocr model...")
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+model = AutoModel.from_pretrained(
+    model_name,
+    trust_remote_code=True,
+    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
+    device_map="auto"
+)
+print("Model loaded!")
+
+def process_image(image):
+    """Process image with dots.ocr"""
+    if image is None:
+        return "Please upload an image"
+
+    try:
+        # Run OCR
+        result = model.generate(image, tokenizer)
+        return result
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=process_image,
+    inputs=gr.Image(type="pil", label="Upload Image"),
+    outputs=gr.Textbox(label="OCR Result", lines=10),
+    title="dots.ocr - Multilingual OCR",
+    description="Upload an image to extract text using dots.ocr. Supports 100+ languages, tables, formulas, and complex layouts.",
+    examples=[
+        ["examples/example1.jpg"],
+        ["examples/example2.png"]
+    ],
+    theme="soft"
+)
+
+if __name__ == "__main__":
+    iface.launch()

requirements.txt

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+gradio==4.44.0
+transformers==4.45.0
+torch
+torchvision
+pillow
+accelerate
+sentencepiece