huggingface--transformers/docs/source/en/model_doc/pp_formulanet.md

This model was contributed to Hugging Face Transformers on 2026-04-30.

PP-FormulaNet

Overview

PP-FormulaNet-L and PP-FormulaNet_plus-L are part of a series of dedicated lightweight models for table structure recognition, focusing on accurately recognizing table structures in documents and natural scenes. For more details about the SLANet series model, please refer to the official documentation.

Usage

Single input inference

The example below demonstrates how to detect text with PP-PP-FormulaNet_plus-L using the [AutoModel].

from io import BytesIO

import httpx
from PIL import Image
from transformers import AutoProcessor, AutoModelForImageTextToText

model_path = "PaddlePaddle/PP-FormulaNet_plus-L_safetensors" # or "PaddlePaddle/PP-FormulaNet-L_safetensors"
model = AutoModelForImageTextToText.from_pretrained(model_path, device_map="auto")
processor = AutoProcessor.from_pretrained(model_path)

image_url = "https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_formula_rec_001.png"
image = Image.open(BytesIO(httpx.get(image_url).content)).convert("RGB")
inputs = processor(images=image, return_tensors="pt").to(model.device)
outputs = model(**inputs)
result = processor.post_process(outputs)
print(result)

PPFormulaNetConfig

autodoc PPFormulaNetConfig

PPFormulaNetForConditionalGeneration

autodoc PPFormulaNetForConditionalGeneration

PPFormulaNetTextModel

autodoc PPFormulaNetTextModel

PPFormulaNetVisionModel

autodoc PPFormulaNetVisionModel

PPFormulaNetModel

autodoc PPFormulaNetModel

PPFormulaNetTextConfig

autodoc PPFormulaNetTextConfig

PPFormulaNetVisionConfig

autodoc PPFormulaNetVisionConfig

PPFormulaNetImageProcessor

autodoc PPFormulaNetImageProcessor

PPFormulaNetProcessor

autodoc PPFormulaNetProcessor