Table Transformer (pre-trained for Table Structure Recognition)

Table Transformer (TATR) model trained on FinTabNet.c. It was introduced in the paper Aligning benchmark datasets for table structure recognition by Smock et al. and first released in this repository . Disclaimer: The team releasing Table Transformer did not write a model card for this model so this model card has been written by the Hugging Face team.

Model description

The Table Transformer is equivalent to DETR , a Transformer-based object detection model. Note that the authors decided to use the "normalize before" setting of DETR, which means that layernorm is applied before self- and cross-attention.

Usage

You can use the raw model for detecting tables in documents. See the documentation for more info.

microsoft/table-transformer-structure-recognition-v1.1-fin powered by Hugging Face Inference Toolkit

Send Request

You can use cURL or any REST Client to send a request to the AzureML endpoint with your AzureML token.

curl <AZUREML_ENDPOINT_URL> \
    -X POST \
    -H "Authorization: Bearer <AZUREML_TOKEN>" \
    -H "Content-Type: image/jpeg" \
    --data-binary @"image.jpg"

Supported Parameters

inputs (string): The input image data as a base64-encoded string. If no parameters are provided, you can also provide the image data as a raw bytes payload.
parameters (object):
- threshold (float): The probability necessary to make a prediction.

Check the full API Specification at the Hugging Face Inference documentation .

Model Specifications

LicenseMit

Last UpdatedAugust 2025

ProviderHuggingFace

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