Table Extraction

Extract structured table data from images and documents, converting tabular content into machine-readable format.

Generalized Table Extraction

POST https://api.aigen.online/aiscript/table-exraction/v1

Request Body

Name	Type	Description
image*	String	Input base64 images encoded utf-8

200: OK Successful response

{
  "status": "200",
  "message": "success",
  "response_id": "string",
  "error_list": [
    {
      "code": "string",
      "message": "string"
    }
  ],
  "result": [
    [
      {
        "cells": [
          {
            "text": "order",
            "confidence": 0.99875,
            "row": 1,
            "column": 1,
            "bbox": [
              [
                0,
                1
              ],
              [
                1,
                0
              ],
              [
                1,
                1
              ],
              [
                0,
                1
              ]
            ]
          }
        ],
        "n_row": 5,
        "n_column": 5
      }
    ]
  ]
}

400: Bad Request Bad request

{
  "status": "200",
  "message": "success",
  "response_id": "string",
  "error_list": [
    {
      "code": "string",
      "message": "string"
    }
  ],
  "result": [
    [
      {
        "cells": [
          {
            "text": "order",
            "confidence": 0.99875,
            "row": 1,
            "column": 1,
            "bbox": [
              [
                0,
                1
              ],
              [
                1,
                0
              ],
              [
                1,
                1
              ],
              [
                0,
                1
              ]
            ]
          }
        ],
        "n_row": 5,
        "n_column": 5
      }
    ]
  ]
}

Example code

Python

import requests
import json

api = "https://api.aigen.online/aiscript/table-exraction/v1"
headers = {"x-aigen-key": "<key>", "content-type": "application/json"}
data = json.dumps({"image": "<base64_string>"})

res = requests.post(api, data=data, headers=headers)
print(res.json())

Nodejs

const axios = require("axios");

const api = "https://api.aigen.online/aiscript/table-exraction/v1";
const headers = {
  "x-aigen-key": "<key>",
};
const data = { image: "<base64_string>" };

axios
  .post(api, data, { headers: headers })
  .then((res) => {
    console.log(res.data);
  })
  .catch((err) => {
    console.error(err.response.data);
  });

PHP

<?php

$curl = curl_init();

curl_setopt_array($curl, array(
  CURLOPT_URL => 'https://api.aigen.online/aiscript/table-exraction/v1',
  CURLOPT_RETURNTRANSFER => true,
  CURLOPT_CUSTOMREQUEST => 'POST',
  CURLOPT_POSTFIELDS =>'{
    "image": "<base64_string>"
}',
  CURLOPT_HTTPHEADER => array(
    'X-AIGEN-KEY: <aigen-key>',
    'Content-Type: application/json'
  ),
));

$response = curl_exec($curl);

curl_close($curl);
echo $response;
?>

cURL

curl --location 'https://api.aigen.online/aiscript/table-exraction/v1' \
--header 'X-AIGEN-KEY: <aigen-key>' \
--header 'Content-Type: application/json' \
--data '{
    "image": "<base64_string>"
}'