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Receipt OCR Python

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The Python OCR SDK supports the Receipt API.

Using the sample below, we are going to illustrate how to extract the data that we want using the OCR SDK.
Receipt sample

Quick-Start

#
# Install the Python client library by running:
# pip install mindee
#

from mindee import Client, PredictResponse, product

# Init a new client
mindee_client = Client(api_key="my-api-key")

# Load a file from disk
input_doc = mindee_client.source_from_path("/path/to/the/file.ext")

# Load a file from disk and parse it.
result: PredictResponse = mindee_client.parse(
    product.ReceiptV5,
    input_doc,
)

# Print a summary of the API result
print(result.document)

# Print the document-level summary
# print(result.document.inference.prediction)

You can also call this product asynchronously:

#
# Install the Python client library by running:
# pip install mindee
#

from mindee import Client, product, AsyncPredictResponse

# Init a new client
mindee_client = Client(api_key="my-api-key")

# Load a file from disk
input_doc = mindee_client.source_from_path("/path/to/the/file.ext")

# Load a file from disk and enqueue it.
result: AsyncPredictResponse = mindee_client.enqueue_and_parse(
    product.ReceiptV5,
    input_doc,
)

# Print a brief summary of the parsed data
print(result.document)

Output (RST):

########
Document
########
:Mindee ID: d96fb043-8fb8-4adc-820c-387aae83376d
:Filename: default_sample.jpg

Inference
#########
:Product: mindee/expense_receipts v5.3
:Rotation applied: Yes

Prediction
==========
:Expense Locale: en-GB; en; GB; GBP;
:Purchase Category: food
:Purchase Subcategory: restaurant
:Document Type: EXPENSE RECEIPT
:Purchase Date: 2016-02-26
:Purchase Time: 15:20
:Total Amount: 10.20
:Total Net: 8.50
:Total Tax: 1.70
:Tip and Gratuity:
:Taxes:
  +---------------+--------+----------+---------------+
  | Base          | Code   | Rate (%) | Amount        |
  +===============+========+==========+===============+
  | 8.50          | VAT    | 20.00    | 1.70          |
  +---------------+--------+----------+---------------+
:Supplier Name: Clachan
:Supplier Company Registrations: Type: VAT NUMBER, Value: 232153895
                                 Type: VAT NUMBER, Value: 232153895
:Supplier Address: 34 Kingley Street W1B 50H
:Supplier Phone Number: 02074940834
:Receipt Number: 54/7500
:Line Items:
  +--------------------------------------+----------+--------------+------------+
  | Description                          | Quantity | Total Amount | Unit Price |
  +======================================+==========+==============+============+
  | Meantime Pale                        | 2.00     | 10.20        |            |
  +--------------------------------------+----------+--------------+------------+

Page Predictions
================

Page 0
------
:Expense Locale: en-GB; en; GB; GBP;
:Purchase Category: food
:Purchase Subcategory: restaurant
:Document Type: EXPENSE RECEIPT
:Purchase Date: 2016-02-26
:Purchase Time: 15:20
:Total Amount: 10.20
:Total Net: 8.50
:Total Tax: 1.70
:Tip and Gratuity:
:Taxes:
  +---------------+--------+----------+---------------+
  | Base          | Code   | Rate (%) | Amount        |
  +===============+========+==========+===============+
  | 8.50          | VAT    | 20.00    | 1.70          |
  +---------------+--------+----------+---------------+
:Supplier Name: Clachan
:Supplier Company Registrations: Type: VAT NUMBER, Value: 232153895
                                 Type: VAT NUMBER, Value: 232153895
:Supplier Address: 34 Kingley Street W1B 50H
:Supplier Phone Number: 02074940834
:Receipt Number: 54/7500
:Line Items:
  +--------------------------------------+----------+--------------+------------+
  | Description                          | Quantity | Total Amount | Unit Price |
  +======================================+==========+==============+============+
  | Meantime Pale                        | 2.00     | 10.20        |            |
  +--------------------------------------+----------+--------------+------------+

Field Types

Standard Fields

These fields are generic and used in several products.

BaseField

Each prediction object contains a set of fields that inherit from the generic BaseField class.
A typical BaseField object will have the following attributes:

  • value (Union[float, str]): corresponds to the field value. Can be None if no value was extracted.
  • confidence (float): the confidence score of the field prediction.
  • bounding_box ([Point, Point, Point, Point]): contains exactly 4 relative vertices (points) coordinates of a right rectangle containing the field in the document.
  • polygon (List[Point]): contains the relative vertices coordinates (Point) of a polygon containing the field in the image.
  • page_id (int): the ID of the page, always None when at document-level.
  • reconstructed (bool): indicates whether an object was reconstructed (not extracted as the API gave it).

Note: A Point simply refers to a List of two numbers ([float, float]).

Aside from the previous attributes, all basic fields have access to a custom __str__ method that can be used to print their value as a string.

AmountField

The amount field AmountField only has one constraint: its value is an Optional[float].

ClassificationField

The classification field ClassificationField does not implement all the basic BaseField attributes. It only implements value, confidence and page_id.

Note: a classification field's value is always a str`.

CompanyRegistrationField

Aside from the basic BaseField attributes, the company registration field CompanyRegistrationField also implements the following:

  • type (str): the type of company.

DateField

Aside from the basic BaseField attributes, the date field DateField also implements the following:

  • date_object (Date): an accessible representation of the value as a python object. Can be None.

LocaleField

The locale field LocaleField only implements the value, confidence and page_id base BaseField attributes, but it comes with its own:

  • language (str): ISO 639-1 language code (e.g.: en for English). Can be None.
  • country (str): ISO 3166-1 alpha-2 or ISO 3166-1 alpha-3 code for countries (e.g.: GRB or GB for "Great Britain"). Can be None.
  • currency (str): ISO 4217 code for currencies (e.g.: USD for "US Dollars"). Can be None.

StringField

The text field StringField only has one constraint: its value is an Optional[str].

TaxesField

Tax

Aside from the basic BaseField attributes, the tax field TaxField also implements the following:

  • rate (float): the tax rate applied to an item expressed as a percentage. Can be None.
  • code (str): tax code (or equivalent, depending on the origin of the document). Can be None.
  • basis (float): base amount used for the tax. Can be None.
  • value (float): the value of the tax. Can be None.

Note: currently TaxField is not used on its own, and is accessed through a parent Taxes object, a list-like structure.

Taxes (Array)

The Taxes field represents a list-like collection of TaxField objects. As it is the representation of several objects, it has access to a custom __str__ method that can render a TaxField object as a table line.

Specific Fields

Fields which are specific to this product; they are not used in any other product.

Line Items Field

List of all line items on the receipt.

A ReceiptV5LineItem implements the following attributes:

  • description (str): The item description.
  • quantity (float): The item quantity.
  • total_amount (float): The item total amount.
  • unit_price (float): The item unit price.

Attributes

The following fields are extracted for Receipt V5:

Purchase Category

category (ClassificationField): The purchase category of the receipt.

Possible values include:

  • 'toll'
  • 'food'
  • 'parking'
  • 'transport'
  • 'accommodation'
  • 'gasoline'
  • 'telecom'
  • 'miscellaneous'
print(result.document.inference.prediction.category.value)

Purchase Date

date (DateField): The date the purchase was made.

print(result.document.inference.prediction.date.value)

Document Type

document_type (ClassificationField): The type of receipt: EXPENSE RECEIPT or CREDIT CARD RECEIPT.

Possible values include:

  • 'EXPENSE RECEIPT'
  • 'CREDIT CARD RECEIPT'
print(result.document.inference.prediction.document_type.value)

Line Items

line_items (List[ReceiptV5LineItem]): List of all line items on the receipt.

for line_items_elem in result.document.inference.prediction.line_items:
    print(line_items_elem)

Expense Locale

locale (LocaleField): The locale of the document.

print(result.document.inference.prediction.locale.value)

Receipt Number

receipt_number (StringField): The receipt number or identifier.

print(result.document.inference.prediction.receipt_number.value)

Purchase Subcategory

subcategory (ClassificationField): The purchase subcategory of the receipt for transport and food.

Possible values include:

  • 'plane'
  • 'taxi'
  • 'train'
  • 'restaurant'
  • 'shopping'
  • None
print(result.document.inference.prediction.subcategory.value)

Supplier Address

supplier_address (StringField): The address of the supplier or merchant.

print(result.document.inference.prediction.supplier_address.value)

Supplier Company Registrations

supplier_company_registrations (List[CompanyRegistrationField]): List of company registration numbers associated to the supplier.

for supplier_company_registrations_elem in result.document.inference.prediction.supplier_company_registrations:
    print(supplier_company_registrations_elem.value)

Supplier Name

supplier_name (StringField): The name of the supplier or merchant.

print(result.document.inference.prediction.supplier_name.value)

Supplier Phone Number

supplier_phone_number (StringField): The phone number of the supplier or merchant.

print(result.document.inference.prediction.supplier_phone_number.value)

Taxes

taxes (List[TaxField]): The list of taxes present on the receipt.

for taxes_elem in result.document.inference.prediction.taxes:
    print(taxes_elem.polygon)

Purchase Time

time (StringField): The time the purchase was made.

print(result.document.inference.prediction.time.value)

Tip and Gratuity

tip (AmountField): The total amount of tip and gratuity.

print(result.document.inference.prediction.tip.value)

Total Amount

total_amount (AmountField): The total amount paid: includes taxes, discounts, fees, tips, and gratuity.

print(result.document.inference.prediction.total_amount.value)

Total Net

total_net (AmountField): The net amount paid: does not include taxes, fees, and discounts.

print(result.document.inference.prediction.total_net.value)

Total Tax

total_tax (AmountField): The sum of all taxes.

print(result.document.inference.prediction.total_tax.value)

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Updated about 1 month ago