PaddlePaddle/SLANeXt_wired_safetensors

SLANeXt_wired

Introduction

Table structure recognition is an important component of table recognition systems, capable of converting non-editable table images into editable table formats (such as HTML). The goal of table structure recognition is to identify the positions of rows, columns, and cells in tables. The performance of this module directly affects the accuracy and efficiency of the entire table recognition system. The table structure recognition module usually outputs HTML code for the table area, which is then passed as input to the tabl recognition pipeline for further processing.

Model	Accuracy (%)	GPU Inference Time (ms) [Normal Mode / High Performance Mode]	CPU Inference Time (ms) [Normal Mode / High Performance Mode]	Model Storage Size (M)
SLANeXt_wired	69.65	--	--	351M

Note: The accuracy of SLANeXt_wired comes from the results of joint testing with SLANeXt_wireless.

Model Usage

import requests
from PIL import Image
from transformers import AutoImageProcessor, AutoModelForTableRecognition

model_path="PaddlePaddle/SLANeXt_wired_safetensors"
model = AutoModelForTableRecognition.from_pretrained(model_path, dtype=torch.float32, device_map="auto")
image_processor = AutoImageProcessor.from_pretrained(model_path)

image = Image.open(requests.get("https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/table_recognition.jpg", stream=True).raw)
inputs = image_processor(images=image, return_tensors="pt").to(model.device)
outputs = model(**inputs)

results = image_processor.post_process_table_recognition(outputs)

print(result['structure'])
print(result['structure_score'])