TB-OCR: an end-to-end OCR model handling text, math latex, and markdown formats all at once
Model Summary
TB-OCR-preview (Text Block OCR), created by Yifei Hu, is an end-to-end OCR model handling text, math latex, and markdown formats all at once. The model takes a block of text as the input and returns clean markdown output. Headers are marked with ##. Math expressions are guaranteed to be wrapped in brackets \( inline math \) \[ display math \] for easier parsing. This model does not require line-detection or math formula detection.
Running the model in 4-bit only requires ~2.8GB VRAM to load and exhibits little to none degradation.
Use Case (Important!)
This model is NOT designed to perform OCR on full pages. Please consider combining TFT-ID-1.0[HF], a text/tale/figure detection model, for full page OCR. It's also faster to split the larger text blocks into smaller ones and perform OCR in parallel (batch inference).
Sample Usage
# check out https://huggingface.co/microsoft/Phi-3.5-vision-instruct for more details
import torch
from transformers import AutoModelForCausalLM, AutoProcessor, BitsAndBytesConfig
from PIL import Image
import requests
model_id = "yifeihu/TB-OCR-preview-0.1"
DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = AutoModelForCausalLM.from_pretrained(
model_id,
device_map="cuda",
trust_remote_code=True,
torch_dtype="auto",
_attn_implementation='flash_attention_2',
quantization_config=BitsAndBytesConfig(load_in_4bit=True) # Optional: Load model in 4-bit mode to save memory
)
processor = AutoProcessor.from_pretrained(model_id,
trust_remote_code=True,
num_crops=16
)
def phi_ocr(image_url):
question = "Convert the text to markdown format." # this is required
image = Image.open(requests.get(image_url, stream=True).raw)
prompt_message = [{
'role': 'user',
'content': f'<|image_1|>\n{question}',
}]
prompt = processor.tokenizer.apply_chat_template(prompt_message, tokenize=False, add_generation_prompt=True)
inputs = processor(prompt, [image], return_tensors="pt").to("cuda")
generation_args = {
"max_new_tokens": 1024,
"temperature": 0.1,
"do_sample": False
}
generate_ids = model.generate(**inputs, eos_token_id=processor.tokenizer.eos_token_id, **generation_args
)
generate_ids = generate_ids[:, inputs['input_ids'].shape[1]:]
response = processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
response = response.split("<image_end>")[0] # remove the image_end token
return response
test_image_url = "https://huggingface.co/yifeihu/TB-OCR-preview-0.1/resolve/main/sample_input_1.png?download=true"
response = phi_ocr(test_image_url)
print(response)
About this preview checkpoint
This is a preview model to verify the quality of a dataset from a synthetic data pipeline. The preview checkpoint only used ~250k image-text pairs (~50M tokens).
The current model is based on Phi-3.5-vision. Smaller models with even stronger performance are currently being trained or tested.