Data extraction
How to successfully extract data from documents
Before extracting data you need to:
Once you successfully completed the above steps, you can use the /extract-data endpoint to extract data.
We prepared a recipe with steps to help you out.
✔️
Extract data
Open Recipe
Supported file types
typless supports the following file types:
- JPG
- PNG
- TIFF
If you are working with scanned documents we recommend using a resolution of 300 DPI to achieve optimal results.
Are you having problems with document quailty?
We wrote a short blog on how to solve your problems here.
Line item extraction
If you want to extract line items, make sure you defined the line-item structure in the document type.
Request parameters
| Param | Type | Required | Details |
|---|---|---|---|
| document_type_name | string | YES | Name of the document type that you use for extraction. |
| file | string (Base64 encoded file) | YES | The original file of the document that you are extracting data from. |
| file_name | string | YES | Name of the original file of the document that you are extracting data from. Name must include file type suffix e.g. document.pdf |
| customer | string | NO | Your internal customer identification, used for .csv usage report. e.g. "my customer" |
Understanding response
In the response, you will always find all of the fields you defined in the document type. Values for fields will be sorted by the confidence score. If the field is not present on the document the value will be set to null.
Response base params
| Param | Type | Details |
|---|---|---|
| file_name | string | Same value as provided in request |
| object_id | string | Id of document for sending feed-back to dataset. |
| extracted_fields | list | List of extracted fields |
| customer | string | Same value as provided in request |
Extracted fields params
| Param | Type | Behaviour |
|---|---|---|
| name | string | Name of the field you defined in the document type |
| values | list | List of values for the field |
| data_type | string | Type of the field you defined in the document type |
Extracted values params
| Param | Type | Behaviour |
|---|---|---|
| x | int | Top left bounding box corner. If value is null this value will be -1 |
| y | int | Top left bounding box corner. If value is null this bounding box corner value will be -1 |
| width | int | Bounding box width. If value is null this width will be -1 |
| height | int | Bounding box height. If value is null this height will be -1 |
| value | string | Value for field in standard format |
| confidence_score | string | Value between 0 and 1. Bigger the value more confident the system is |
| page_number | int | Page on which value is present. If value is null this page number will be -1 |
Field supplier_name will never cary any positional data - Just value!
All the positional parameters will always be -1, except for confidence_score and the value.
Example full response
{
"file_name": "invoice_2.pdf",
"object_id": "1cb25cc8-c9fa-4149-9a83-b4ed6a2173b9",
"extracted_fields": [
{
"name": "supplier",
"values": [
{
"x": -1,
"y": -1,
"width": -1,
"height": -1,
"value": "ScaleGrid",
"confidence_score": "0.968",
"page_number": -1
}
],
"data_type": "AUTHOR"
},
{
"name": "invoice_number",
"values": [
{
"x": 1989,
"y": 545,
"width": 323,
"height": 54,
"value": "20190500005890",
"confidence_score": "0.250",
"page_number": 0
},
{
"x": 167,
"y": 574,
"width": 391,
"height": 54,
"value": "GB123456789",
"confidence_score": "0.250",
"page_number": 0
}
],
"data_type": "STRING"
},
{
"name": "issue_date",
"values": [
{
"x": 2072,
"y": 628,
"width": 240,
"height": 54,
"value": "2019-06-05",
"confidence_score": "0.358",
"page_number": 0
}
],
"data_type": "DATE"
},
{
"name": "total_amount",
"values": [
{
"x": 2146,
"y": 1196,
"width": 126,
"height": 54,
"value": "47.5300",
"confidence_score": "0.990",
"page_number": 0
}
],
"data_type": "NUMBER"
}
],
"line_items": [
[
{
"name": "Description",
"values": [
{
"x": 208,
"y": 1196,
"width": 1022,
"height": 50,
"value": "5/2019-MongoBackend-MgmtStandalone-Small-744 hours",
"confidence_score": "0.661",
"page_number": 0
}
],
"data_type": "STRING"
},
{
"name": "Price",
"values": [
{
"x": 2146,
"y": 1196,
"width": 126,
"height": 54,
"value": "47.5300",
"confidence_score": "0.582",
"page_number": 0
}
],
"data_type": "NUMBER"
},
{
"name": "Quantity",
"values": [
{
"x": 1979,
"y": 1196,
"width": 23,
"height": 54,
"value": "1",
"confidence_score": "0.647",
"page_number": 0
}
],
"data_type": "NUMBER"
}
]
],
"customer": null
}
Example null response
{
"file_name": "invoice.pdf",
"object_id": "26e01d82-e7f4-48d3-a902-b74283b73279",
"extracted_fields": [
{
"name": "total_amount",
"values": [
{
"x": -1,
"y": -1,
"width": -1,
"height": -1,
"value": null,
"confidence_score": "0.000",
"page_number": -1
}
],
"data_type": "NUMBER"
},
{
"name": "invoice_number",
"values": [
{
"x": -1,
"y": -1,
"width": -1,
"height": -1,
"value": null,
"confidence_score": "0.000",
"page_number": -1
}
],
"data_type": "STRING"
},
{
"name": "issue_date",
"values": [
{
"x": -1,
"y": -1,
"width": -1,
"height": -1,
"value": null,
"confidence_score": "0.000",
"page_number": -1
}
],
"data_type": "DATE"
},
{
"name": "supplier",
"values": [
{
"x": -1,
"y": -1,
"width": -1,
"height": -1,
"value": null,
"confidence_score": "0.000",
"page_number": -1
}
],
"data_type": "AUTHOR"
}
],
"line_items": [],
"customer": null
}
Updated 8 months ago