Verbosity
Configures the verbosity of the extraction.
Parameters
Possible Values | Description |
---|---|
0 | default |
1 | Adds lines array to field output, with text , page , and boundingPoly metadata describing the text ouput in the field's value . |
3 | - Adds full standardized text metadata (anchor, lines, points, regions) - If Sensible extracted a field from OCR'd text, then Sensible adds the following confidence scores to the output. Sensible's OCR engines output confidence scores between 0 and 1: 1. In the field's standardized text metadata, Sensible provides a confidence score for each element in the lines array. The score is the average score of the "tokens", or words, in the line.2. In the field output, Sensible outputs anchorConfidence and valueConfidence values, which are the averages of their source lines' confidence scores.For more information about using confidence scores, see Validate extractions. If the standardized text metadata causes the extraction response to exceed 6MB, as can be the case with large documents, then Sensible returns a 413 error. |
Examples
If you add "verbosity": 1,
as a sibling to the fields array in the Table example, you get the following output:
{
"agile_risks_table_updates_monthly": {
"columns": [
{
"id": "col1_risk_description",
"values": [
{
"value": "Poor task point estimation",
"type": "string",
"lines": [
{
"text": "Poor task point estimation",
"boundingPolygon": [
{
"x": 0.992,
"y": 2.972
},
{
"x": 2.354,
"y": 2.972
},
{
"x": 2.354,
"y": 3.882
},
{
"x": 0.992,
"y": 3.882
}
],
"page": 0
}
]
},
{
"value": "Poor epic scope definition",
"type": "string",
"lines": [
{
"text": "Poor epic scope definition",
"boundingPolygon": [
{
"x": 0.992,
"y": 3.882
},
{
"x": 2.354,
"y": 3.882
},
{
"x": 2.354,
"y": 4.799
},
{
"x": 0.992,
"y": 4.799
}
],
"page": 0
}
]
},
{
"value": "Inadequate scrum master training",
"type": "string",
"lines": [
{
"text": "Inadequate scrum master training",
"boundingPolygon": [
{
"x": 0.992,
"y": 4.799
},
{
"x": 2.354,
"y": 4.799
},
{
"x": 2.354,
"y": 5.964
},
{
"x": 0.992,
"y": 5.964
}
],
"page": 0
}
]
}
]
},
{
"id": "rank_this_month",
"values": [
{
"source": "3",
"value": 3,
"type": "number",
"lines": [
{
"text": "3",
"boundingPolygon": [
{
"x": 6.625,
"y": 2.972
},
{
"x": 8.036,
"y": 2.972
},
{
"x": 8.036,
"y": 3.882
},
{
"x": 6.625,
"y": 3.882
}
],
"page": 0
}
]
},
{
"source": "1",
"value": 1,
"type": "number",
"lines": [
{
"text": "1",
"boundingPolygon": [
{
"x": 6.625,
"y": 3.882
},
{
"x": 8.036,
"y": 3.882
},
{
"x": 8.036,
"y": 4.799
},
{
"x": 6.625,
"y": 4.799
}
],
"page": 0
}
]
},
{
"source": "2",
"value": 2,
"type": "number",
"lines": [
{
"text": "2",
"boundingPolygon": [
{
"x": 6.625,
"y": 4.799
},
{
"x": 8.036,
"y": 4.799
},
{
"x": 8.036,
"y": 5.964
},
{
"x": 6.625,
"y": 5.964
}
],
"page": 0
}
]
}
]
}
]
}
}
Updated about 8 hours ago