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✨ add support for business cards, delivery notes, indian passport & update resume (#272)
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‎docs/extras/code_samples/business_card_v1_async.txt‎

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from mindee import Client, product, AsyncPredictResponse
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# Init a new client
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mindee_client = Client(api_key="my-api-key")
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# Load a file from disk
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input_doc = mindee_client.source_from_path("/path/to/the/file.ext")
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# Load a file from disk and enqueue it.
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result: AsyncPredictResponse = mindee_client.enqueue_and_parse(
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product.BusinessCardV1,
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input_doc,
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)
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# Print a brief summary of the parsed data
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print(result.document)

‎docs/extras/code_samples/delivery_notes_v1_async.txt‎

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from mindee import Client, product, AsyncPredictResponse
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# Init a new client
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mindee_client = Client(api_key="my-api-key")
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# Load a file from disk
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input_doc = mindee_client.source_from_path("/path/to/the/file.ext")
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# Load a file from disk and enqueue it.
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result: AsyncPredictResponse = mindee_client.enqueue_and_parse(
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product.DeliveryNoteV1,
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input_doc,
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)
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# Print a brief summary of the parsed data
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print(result.document)

‎docs/extras/code_samples/ind_passport_v1_async.txt‎

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from mindee import Client, product, AsyncPredictResponse
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# Init a new client
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mindee_client = Client(api_key="my-api-key")
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# Load a file from disk
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input_doc = mindee_client.source_from_path("/path/to/the/file.ext")
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# Load a file from disk and enqueue it.
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result: AsyncPredictResponse = mindee_client.enqueue_and_parse(
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product.ind.IndianPassportV1,
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input_doc,
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)
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# Print a brief summary of the parsed data
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print(result.document)

‎docs/extras/guide/business_card_v1.md‎

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---
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title: Business Card OCR Python
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category: 622b805aaec68102ea7fcbc2
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slug: python-business-card-ocr
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parentDoc: 609808f773b0b90051d839de
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---
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The Python OCR SDK supports the [Business Card API](https://platform.mindee.com/mindee/business_card).
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Using the [sample below](https://github.com/mindee/client-lib-test-data/blob/main/products/business_card/default_sample.jpg), we are going to illustrate how to extract the data that we want using the OCR SDK.
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![Business Card sample](https://github.com/mindee/client-lib-test-data/blob/main/products/business_card/default_sample.jpg?raw=true)
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# Quick-Start
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```py
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from mindee import Client, product, AsyncPredictResponse
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# Init a new client
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mindee_client = Client(api_key="my-api-key")
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# Load a file from disk
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input_doc = mindee_client.source_from_path("/path/to/the/file.ext")
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# Load a file from disk and enqueue it.
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result: AsyncPredictResponse = mindee_client.enqueue_and_parse(
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product.BusinessCardV1,
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input_doc,
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)
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# Print a brief summary of the parsed data
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print(result.document)
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```
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**Output (RST):**
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```rst
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########
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Document
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########
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:Mindee ID: 6f9a261f-7609-4687-9af0-46a45156566e
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:Filename: default_sample.jpg
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Inference
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#########
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:Product: mindee/business_card v1.0
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:Rotation applied: Yes
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Prediction
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==========
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:Firstname: Andrew
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:Lastname: Morin
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:Job Title: Founder & CEO
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:Company: RemoteGlobal
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:Email: amorin@remoteglobalconsulting.com
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:Phone Number: +14015555555
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:Mobile Number: +13015555555
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:Fax Number: +14015555556
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:Address: 178 Main Avenue, Providence, RI 02111
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:Website: www.remoteglobalconsulting.com
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:Social Media: https://www.linkedin.com/in/johndoe
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https://twitter.com/johndoe
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```
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# Field Types
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## Standard Fields
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These fields are generic and used in several products.
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### BaseField
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Each prediction object contains a set of fields that inherit from the generic `BaseField` class.
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A typical `BaseField` object will have the following attributes:
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* **value** (`Union[float, str]`): corresponds to the field value. Can be `None` if no value was extracted.
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* **confidence** (`float`): the confidence score of the field prediction.
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* **bounding_box** (`[Point, Point, Point, Point]`): contains exactly 4 relative vertices (points) coordinates of a right rectangle containing the field in the document.
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* **polygon** (`List[Point]`): contains the relative vertices coordinates (`Point`) of a polygon containing the field in the image.
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* **page_id** (`int`): the ID of the page, always `None` when at document-level.
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* **reconstructed** (`bool`): indicates whether an object was reconstructed (not extracted as the API gave it).
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> **Note:** A `Point` simply refers to a List of two numbers (`[float, float]`).
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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.
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### StringField
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The text field `StringField` only has one constraint: its **value** is an `Optional[str]`.
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# Attributes
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The following fields are extracted for Business Card V1:
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## Address
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**address** ([StringField](#stringfield)): The address of the person.
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```py
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print(result.document.inference.prediction.address.value)
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```
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## Company
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**company** ([StringField](#stringfield)): The company the person works for.
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```py
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print(result.document.inference.prediction.company.value)
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```
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## Email
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**email** ([StringField](#stringfield)): The email address of the person.
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```py
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print(result.document.inference.prediction.email.value)
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```
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## Fax Number
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**fax_number** ([StringField](#stringfield)): The Fax number of the person.
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```py
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print(result.document.inference.prediction.fax_number.value)
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```
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## Firstname
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**firstname** ([StringField](#stringfield)): The given name of the person.
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```py
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print(result.document.inference.prediction.firstname.value)
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```
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## Job Title
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**job_title** ([StringField](#stringfield)): The job title of the person.
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```py
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print(result.document.inference.prediction.job_title.value)
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```
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## Lastname
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**lastname** ([StringField](#stringfield)): The lastname of the person.
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```py
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print(result.document.inference.prediction.lastname.value)
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```
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## Mobile Number
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**mobile_number** ([StringField](#stringfield)): The mobile number of the person.
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```py
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print(result.document.inference.prediction.mobile_number.value)
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```
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## Phone Number
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**phone_number** ([StringField](#stringfield)): The phone number of the person.
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```py
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print(result.document.inference.prediction.phone_number.value)
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```
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## Social Media
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**social_media** (List[[StringField](#stringfield)]): The social media profiles of the person or company.
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```py
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for social_media_elem in result.document.inference.prediction.social_media:
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print(social_media_elem.value)
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```
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## Website
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**website** ([StringField](#stringfield)): The website of the person or company.
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```py
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print(result.document.inference.prediction.website.value)
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```
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# Questions?
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[Join our Slack](https://join.slack.com/t/mindee-community/shared_invite/zt-2d0ds7dtz-DPAF81ZqTy20chsYpQBW5g)

‎docs/extras/guide/delivery_notes_v1.md‎

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---
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title: Delivery note OCR Python
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category: 622b805aaec68102ea7fcbc2
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slug: python-delivery-note-ocr
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parentDoc: 609808f773b0b90051d839de
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---
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The Python OCR SDK supports the [Delivery note API](https://platform.mindee.com/mindee/delivery_notes).
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Using the [sample below](https://github.com/mindee/client-lib-test-data/blob/main/products/delivery_notes/default_sample.jpg), we are going to illustrate how to extract the data that we want using the OCR SDK.
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![Delivery note sample](https://github.com/mindee/client-lib-test-data/blob/main/products/delivery_notes/default_sample.jpg?raw=true)
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# Quick-Start
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```py
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from mindee import Client, product, AsyncPredictResponse
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# Init a new client
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mindee_client = Client(api_key="my-api-key")
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# Load a file from disk
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input_doc = mindee_client.source_from_path("/path/to/the/file.ext")
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# Load a file from disk and enqueue it.
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result: AsyncPredictResponse = mindee_client.enqueue_and_parse(
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product.DeliveryNoteV1,
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input_doc,
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)
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# Print a brief summary of the parsed data
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print(result.document)
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```
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**Output (RST):**
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```rst
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########
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Document
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########
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:Mindee ID: d5ead821-edec-4d31-a69a-cf3998d9a506
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:Filename: default_sample.jpg
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Inference
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#########
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:Product: mindee/delivery_notes v1.0
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:Rotation applied: Yes
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Prediction
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==========
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:Delivery Date: 2019-10-02
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:Delivery Number: INT-001
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:Supplier Name: John Smith
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:Supplier Address: 4490 Oak Drive, Albany, NY 12210
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:Customer Name: Jessie M Horne
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:Customer Address: 4312 Wood Road, New York, NY 10031
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:Total Amount: 204.75
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```
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# Field Types
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## Standard Fields
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These fields are generic and used in several products.
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### BaseField
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Each prediction object contains a set of fields that inherit from the generic `BaseField` class.
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A typical `BaseField` object will have the following attributes:
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* **value** (`Union[float, str]`): corresponds to the field value. Can be `None` if no value was extracted.
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* **confidence** (`float`): the confidence score of the field prediction.
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* **bounding_box** (`[Point, Point, Point, Point]`): contains exactly 4 relative vertices (points) coordinates of a right rectangle containing the field in the document.
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* **polygon** (`List[Point]`): contains the relative vertices coordinates (`Point`) of a polygon containing the field in the image.
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* **page_id** (`int`): the ID of the page, always `None` when at document-level.
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* **reconstructed** (`bool`): indicates whether an object was reconstructed (not extracted as the API gave it).
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> **Note:** A `Point` simply refers to a List of two numbers (`[float, float]`).
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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.
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### AmountField
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The amount field `AmountField` only has one constraint: its **value** is an `Optional[float]`.
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### DateField
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Aside from the basic `BaseField` attributes, the date field `DateField` also implements the following:
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* **date_object** (`Date`): an accessible representation of the value as a python object. Can be `None`.
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### StringField
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The text field `StringField` only has one constraint: its **value** is an `Optional[str]`.
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# Attributes
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The following fields are extracted for Delivery note V1:
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## Customer Address
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**customer_address** ([StringField](#stringfield)): The address of the customer receiving the goods.
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```py
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print(result.document.inference.prediction.customer_address.value)
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```
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## Customer Name
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**customer_name** ([StringField](#stringfield)): The name of the customer receiving the goods.
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```py
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print(result.document.inference.prediction.customer_name.value)
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```
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## Delivery Date
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**delivery_date** ([DateField](#datefield)): The date on which the delivery is scheduled to arrive.
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```py
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print(result.document.inference.prediction.delivery_date.value)
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```
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## Delivery Number
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**delivery_number** ([StringField](#stringfield)): A unique identifier for the delivery note.
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```py
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print(result.document.inference.prediction.delivery_number.value)
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```
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## Supplier Address
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**supplier_address** ([StringField](#stringfield)): The address of the supplier providing the goods.
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```py
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print(result.document.inference.prediction.supplier_address.value)
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```
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## Supplier Name
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**supplier_name** ([StringField](#stringfield)): The name of the supplier providing the goods.
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```py
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print(result.document.inference.prediction.supplier_name.value)
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```
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## Total Amount
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**total_amount** ([AmountField](#amountfield)): The total monetary value of the goods being delivered.
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```py
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print(result.document.inference.prediction.total_amount.value)
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```
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# Questions?
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[Join our Slack](https://join.slack.com/t/mindee-community/shared_invite/zt-2d0ds7dtz-DPAF81ZqTy20chsYpQBW5g)

‎docs/extras/guide/financial_document_v1.md‎

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########
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Document
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########
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:Mindee ID: 340ee4ae-b4da-41f0-b5ea-81ae29852b57
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:Mindee ID: b26161ce-35d0-4984-b1ff-886645e160e6
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:Filename: default_sample.jpg
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Inference
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#########
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:Product: mindee/financial_document v1.10
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:Product: mindee/financial_document v1.11
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:Rotation applied: Yes
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Prediction
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==========
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:Locale: en; en; USD;
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:Locale: en-US; en; US; USD;
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:Invoice Number: INT-001
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:Purchase Order Number: 2412/2019
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:Receipt Number:
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Page 0
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------
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:Locale: en; en; USD;
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:Locale: en-US; en; US; USD;
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:Invoice Number: INT-001
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:Purchase Order Number: 2412/2019
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:Receipt Number:

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