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Custom OCR .NET

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The .NET OCR SDK supports custom-built API from the API Builder.
If your document isn't covered by one of Mindee's Off-the-Shelf APIs, you can create your own API using the API Builder.

For the following examples, we are using our own W9s custom API created with the API Builder.

Quick Start

using Mindee;
using Mindee.Input;
using Mindee.Http;
using Mindee.Parsing;

string apiKey = "my-api-key";
string filePath = "/path/to/the/file.ext";

// Construct a new client
MindeeClient mindeeClient = new MindeeClient(apiKey);

// Load an input source as a path string
// Other input types can be used, as mentioned in the docs
var inputSource = new LocalInputSource(filePath);

// Set the endpoint configuration
CustomEndpoint myEndpoint = new CustomEndpoint(
    endpointName: "my-endpoint",
    accountName: "my-account"
    // optionally, lock the version
    //, version: "1.1"
);

var response = await mindeeClient.ParseAsync(
    inputSource, myEndpoint);

// Print a summary of the predictions
System.Console.WriteLine(response.Document.ToString());

// Print the document-level predictions
// System.Console.WriteLine(response.Document.Inference.Prediction.ToString());

If the version argument is set, you'll be required to update it every time a new model is trained.
This is probably not needed for development, but essential for production use.

Parsing Documents

Use the ParseAsync method to call the API prediction on your custom document.
The response class and document type must be specified when calling this method.

You have two different ways to parse a custom document.

  1. Don't specify the return class, and use the default one (named CustomPrediction):
var response = await _mindeeClient
    .LoadDocument(new FileInfo(Path))
    .ParseAsync(myEndpoint);
  1. You can also use your own class which will represent the required fields. For example:
using Mindee.Http;

public sealed class WNineV1DocumentPrediction
{
  [JsonPropertyName("name")]
  public StringField Name { get; set; }

  [JsonPropertyName("employer_id")]
  public StringField EmployerId { get; set; }
  
  (...)
}

// The CustomEndpoint attribute is required when using your own model.
// It will be used to know which Mindee API called.
[Endpoint(
    endpointName: "wnine",
    accountName: "john",
    // optionally, lock the version
    //version: "1.1"
)]
public sealed class WNineV1Inference : Inference<WNineV1DocumentPrediction, WNineV1DocumentPrediction>
{
}

var response = await _mindeeClient
    .LoadDocument(new FileInfo(Path))
    .ParseAsync<WNineV1Inference>();

CustomV1Inference object

All the fields which are present in the API builder
are available (the fields are defined when creating your custom API).

CustomV1 is an object which contains a document prediction and pages prediction result.

CustomV1Page

Which is a Dictionnary<String, ListField> with the key as a string for the name of the field, and a ListField as a value.

CustomV1Document

Which contains 2 properties : ClassificationFields and Fields.
Both are a Map and the key is a string for the name of the field and for the value :

  • ClassificationFields have a ClassificationField object as value. Each ClassificationField contains a value.
  • Fields have a ListField object as value. Each ListField contains a list of all values extracted for this field.

📘

Info

Both document level and page level objects work in the same way.

Fields property

A Map with the following structure:

  • Confidence: a double
  • Values: a list of ListFieldValue which containing a list of all values found for the field.

In the examples below we'll use the employer_id field.

Endpoint myEndpoint = new Endpoint(
    endpointName: "wnine",
    accountName: "john",
    // optionally, lock the version
    //version: "1.1"
);

var response = await _mindeeClient
    .LoadDocument(new FileInfo(path))
    .ParseAsync(myEndpoint);

ListField? employerId = response.Document.Inference.Pages.FirstOrDefault()?.Prediction.GetValueOrDefault("employer_id");

Line items reconstructions

We offer the possibility to use post-processing for the prediction result from your custom API.

In the example below, imagine that your custom document has a table with 4 columns.
So, you want to extract all the line items (table rows) from it.

In that case, you will have to define 4 fields and do the annotation vertically for each one.

After training your model, test it using the Mindee client as below to get your document parsed and line items reconstructed.

CustomEndpoint myEndpoint = new CustomEndpoint(
    endpointName: "wnine",
    accountName: "john",
    // optionally, lock the version
    //, version: "1.1"
);

var response = await _mindeeClient
    .LoadDocument(new FileInfo(path))
    .ParseAsync(myEndpoint);

// The fields used to define the columns
var columnFieldNames = new List<string>() {
    "beneficiary_birth_date",
    "beneficiary_number",
    "beneficiary_name",
    "beneficiary_rank"
    };

// You can adjust the height variation of your lines.
// The value will depend on your table of course!
var lineHeigthTolerance = 0.011d;

// The anchor must be the column where there is always a value in your table. 
var anchor = new Anchor("beneficiary_name", lineHeigthTolerance);

var lineItems = LineItemsGenerator.Generate(
    columnFieldNames,
    response.Document.Inference.Prediction.Fields,
    anchor
    );

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Updated about 2 months ago