Prediction Endpoint

Prediction is the main endpoint of Mindee API to extract information from your document. Use the Prediction endpoint by selecting the API you want to use and upload your document. You will then receive JSON predictions at document level or even page level for the available fields.


To make a prediction, select the document API <account_name>/<api_name>/<api_version> where:

  • <account_name> refers to the username or organization name of the account that created the API,
  • <api_name>/<api_version> refers to the name and selected version as described in API Documentation.

Then use the URL:


Off-the-shelf APIs

Mindee ready-to-use APIs are accessible on the account name mindee. You can browse all of them in the API Store.


Off-the-shelf APIs use a major version convention. A new major version may not be fully backward compatible and bring new features and better performance.


Custom APIs

When creating a custom document parsing API with the API Builder, you must train the API before making your first predictions. As the training is progressing, a new minor version is created for each new model deployed:

  • v1.0 - no model / no predictions
  • v1.1 - first model
  • v1.2 - second model
  • ... etc


Select the version v1 to always have the latest and best model.

Example: bob/form_456/v1


The Prediction endpoint can handle three types of payload in order to send your document:

  • a binary file
  • a base64 encoded file
  • a URL

See Document inputs for more information on supported files.

Send a Binary File

Use a multipart/form-data encoding to send your document


curl -X POST<account_name>/<api_name>/<api_version>/predict 
  -H 'Authorization: Token my-token' 
  -F document=@/path/to/your/file.png
import requests

url = "<account_name>/<api_name>/<api_version>/predict"

with open("/path/to/my/file", "rb") as myfile:
    files = {"document": myfile}
    headers = {"Authorization": "Token my-api-key-here"}
    response =, files=files, headers=headers)
using System;
using System.IO;
using System.Net.Http;
using System.Net.Http.Headers;

class Program
    static void Main(string[] args)
        var url = "<account_name>/<api_name>/<api_version>/predict";
        var filePath = @"/path/to/my/file";
        var token = "my-api-key-here";

        var file = File.OpenRead(filePath);
        var streamContent = new StreamContent(file);
        var imageContent = new ByteArrayContent(streamContent.ReadAsByteArrayAsync().Result);
        imageContent.Headers.ContentType = MediaTypeHeaderValue.Parse("multipart/form-data");

        var form = new MultipartFormDataContent();
        form.Add(imageContent, "document", Path.GetFileName(filePath));

        var httpClient = new HttpClient();
        httpClient.DefaultRequestHeaders.Authorization = new AuthenticationHeaderValue("Token", token);
        var response = httpClient.PostAsync(url, form).Result;


The @ in the curl command is very important as it tells curl that you aren’t passing a data but a file.

Send a Base64 Encoded File

Prepare a JSON payload:

  "document": "/9j......"

Send your request with an application/json encoding:

curl -X POST \<account_name>/<api_name>/<api_version>/predict \
  -H 'Authorization: Token my-api-key-here' \
  -H 'Content-Type: application/json' \
  -d 'document="/9j..."'

Send a URL

Prepare a JSON payload:

  "document": ""

Send your request with an application/json encoding:

curl -X POST \<account_name>/<api_name>/<api_version>/predict \
  -H 'Authorization: Token my-token' \
  -H 'Content-Type: application/json' \
  -d '{"document":""}'


Only a public HTTPS URL is accepted.

JSON Response

See Endpoints for general description of Mindee's REST API response format.


When calling the prediction endpoint, the parsed information from your documents can be found in the document key.

  "api_request": { .. }, 
  "document": {
    "id": "ac668055-e7db-48f2-b81f-e5ba9a6a6b8f",
    "name": "myfile.pdf",
    "n_pages": 2,
    "inference": {
      "started_at": "2021-03-24T09:14:27+00:00",
      "finished_at": "2021-03-24T09:14:28+00:00",
      "processing_time": 1.087,
      "is_rotation_applied": true,
      "extras": {},
      "prediction": { .. },
      "pages": [
          "id": 0,
          "orientation": {"value": 0},
          "extras": {},
          "prediction": { .. }
          "id": 1,
          "orientation": {"value": 0},
          "extras": {},
          "prediction": { .. }


Describes the uploaded document

idstringa unique identifier
namestringthe filename
n_pagesnumberthe number of pages
inferenceobjecta JSON object with the content of your inference (prediction)

Document > Inference

Contains the whole inference data (predictions)

started_atstringthe date & time the inference has started in ISO 8601 format
finished_atstringthe date & time the inference was finished in ISO 8601 format
processing_timenumberthe request processing time in seconds
is_rotation_appliedboolean or nulltrue: polygons are already rotated given the page orientation
false: polygons are never rotated
null: the API has no orientation information
extrasobjecta JSON object with document-level extras predictions
predictionobjecta JSON object with the document-level API prediction
pageslist[object]a JSON object with the page-level inference data

Document > Inference > Pages[ ]

Contains the page-level specific inference data (predictions)

idnumberthe page index
orientation.valuenumberthe clockwise rotation to apply to get the page upright
Examples: 0, 90, 180, 270
extrasobjecta JSON object with page-level extras predictions
Example: the Cropper feature
predictionobjecta JSON object with the page-level API prediction

Prediction example

Each API can describe several fields within its prediction object. Depending on the field properties, you will find values, a confidence score or polygons.

  "prediction": {
    "locale": {
      "country": "CA",
      "currency": "CAD",
      "language": "en",
      "value": "en-CA",
      "confidence": 0.85
    "date": {
      "value": "2020-07-03",
      "confidence": 0.99,
      "polygon": [[0.273, 0.355], [0.289, 0.355], [0.289, 0.373], [0.273, 0.373]]
    "total_incl": {
      "value": 14.32,
      "confidence": 0.98,
      "polygon": [[0.581, 0.485], [0.696, 0.485], [0.696, 0.503], [0.581, 0.503]]



To know more about your document parsing API response, especially the prediction object's structure, you can access the Documentation part of your API on Mindee's platform.


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