Going further

With Textractor we strive to offer a solution that comes with everything you need to build your workflow. However, we cannot realistically cover every use case and we anticipate that there will be situations when the library falls short of your expectations and there will be a need to custom solutions.

To do so, every part of the Document object, may it be Page, IdentityDocument, ExpenseDocument contains the the JSON block that was used to instantiate it.

Installation

To begin, install the amazon-textract-textractor package using pip.

pip install amazon-textract-textractor

There are various sets of dependencies available to tailor your installation to your use case. The base package will have sensible default, but you may want to install the PDF extra dependencies if your workflow uses PDFs with pip install amazon-textract-textractor[pdf]. You can read more on extra dependencies in the documentation

Calling Textract

For the sake of diversity, we will use the AnalyzeExpense API for this example, however, the functions and properties showcased here are available on all document types.

from textractor import Textractor

extractor = Textractor(profile_name="default")
# This path assumes that you are running the notebook from docs/source/notebooks
document = extractor.analyze_expense(file_source="../../../tests/fixtures/form.png")

Response dictionary

The response dictionary is the unchanged JSON that was returned by the Textract API, if you save it as a file it will be the same as the JSON you would have gotten from using boto3.

document.response

{'DocumentMetadata': {'Pages': 1},
 'ExpenseDocuments': [{'ExpenseIndex': 1,
   'SummaryFields': [{'Type': {'Text': 'VENDOR_NAME',
      'Confidence': 84.23397827148438},
     'ValueDetection': {'Text': 'Royal',
      'Geometry': {'BoundingBox': {'Width': 0.33433136343955994,
        'Height': 0.1101887971162796,
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        'Top': 0.10576257109642029},
       'Polygon': [{'X': 0.16748881340026855, 'Y': 0.10576257109642029},
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        {'X': 0.5018202066421509, 'Y': 0.2159513682126999},
        {'X': 0.168068990111351, 'Y': 0.2151809185743332}]},
      'Confidence': 83.68743133544922},
     'PageNumber': 1},
    {'Type': {'Text': 'OTHER', 'Confidence': 97.5},
     'LabelDetection': {'Text': '6 Mo',
      'Geometry': {'BoundingBox': {'Width': 0.037598639726638794,
        'Height': 0.010990276001393795,
        'Left': 0.4355943500995636,
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        {'X': 0.435648649930954, 'Y': 0.9142314791679382}]},
      'Confidence': 87.35102081298828},
     'ValueDetection': {'Text': '',
      'Geometry': {'BoundingBox': {'Width': 0.015726225450634956,
        'Height': 0.011696578934788704,
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      'Confidence': 0.0},
     'PageNumber': 1},
    {'Type': {'Text': 'OTHER', 'Confidence': 96.5},
     'LabelDetection': {'Text': 'Permanent Wear',
      'Geometry': {'BoundingBox': {'Width': 0.12020225077867508,
        'Height': 0.010676798410713673,
        'Left': 0.09873555600643158,
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      'Confidence': 96.31539916992188},
     'ValueDetection': {'Text': '',
      'Geometry': {'BoundingBox': {'Width': 0.01464390754699707,
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      'Confidence': 0.0},
     'PageNumber': 1},
    {'Type': {'Text': 'OTHER', 'Confidence': 96.0},
     'LabelDetection': {'Text': '12 Mo',
      'Geometry': {'BoundingBox': {'Width': 0.04926685988903046,
        'Height': 0.01091374084353447,
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      'Confidence': 64.79031372070312},
     'ValueDetection': {'Text': '',
      'Geometry': {'BoundingBox': {'Width': 0.016481652855873108,
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        'Left': 0.4822356104850769,
        'Top': 0.9004089832305908},
       'Polygon': [{'X': 0.4822356104850769, 'Y': 0.9004793167114258},
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      'Confidence': 0.0},
     'PageNumber': 1},
    {'Type': {'Text': 'OTHER', 'Confidence': 95.5},
     'LabelDetection': {'Text': 'Phone',
      'Geometry': {'BoundingBox': {'Width': 0.0415608249604702,
        'Height': 0.010550999082624912,
        'Left': 0.07793831080198288,
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      'Confidence': 95.25244903564453},
     'ValueDetection': {'Text': '615-373-6883',
      'Geometry': {'BoundingBox': {'Width': 0.21608000993728638,
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      'Confidence': 94.58015441894531},
     'PageNumber': 1},
    {'Type': {'Text': 'OTHER', 'Confidence': 95.5},
     'LabelDetection': {'Text': 'Loose Seams',
      'Geometry': {'BoundingBox': {'Width': 0.09154075384140015,
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      'Confidence': 95.17913055419922},
     'ValueDetection': {'Text': '',
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      'Confidence': 0.0},
     'PageNumber': 1},
    {'Type': {'Text': 'OTHER', 'Confidence': 95.0},
     'LabelDetection': {'Text': 'Permanent Shading',
      'Geometry': {'BoundingBox': {'Width': 0.13924720883369446,
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      'Confidence': 94.83258819580078},
     'ValueDetection': {'Text': '',
      'Geometry': {'BoundingBox': {'Width': 0.014641131274402142,
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      'Confidence': 0.0},
     'PageNumber': 1},
    {'Type': {'Text': 'OTHER', 'Confidence': 95.0},
     'LabelDetection': {'Text': 'Allergy Concerns',
      'Geometry': {'BoundingBox': {'Width': 0.11545991897583008,
        'Height': 0.012368067167699337,
        'Left': 0.28926894068717957,
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      'Confidence': 91.31575775146484},
     'ValueDetection': {'Text': '',
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      'Confidence': 0.0},
     'PageNumber': 1},
    {'Type': {'Text': 'OTHER', 'Confidence': 93.0},
     'LabelDetection': {'Text': 'Excessive Wear',
      'Geometry': {'BoundingBox': {'Width': 0.10464227199554443,
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      'Confidence': 92.89436340332031},
     'ValueDetection': {'Text': '',
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      'Confidence': 0.0},
     'PageNumber': 1},
    {'Type': {'Text': 'OTHER', 'Confidence': 92.5},
     'LabelDetection': {'Text': 'Time',
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      'Confidence': 92.33248901367188},
     'ValueDetection': {'Text': '12.30 P.M.',
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      'Confidence': 90.90103912353516},
     'ValueDetection': {'Text': 'JOHN LEWIS',
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      'Confidence': 91.7969970703125},
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    {'Type': {'Text': 'OTHER', 'Confidence': 91.0},
     'LabelDetection': {'Text': 'Cellular',
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      'Confidence': 90.93302154541016},
     'ValueDetection': {'Text': '683-426-2200',
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      'Confidence': 90.27494812011719},
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    {'Type': {'Text': 'OTHER', 'Confidence': 90.5},
     'LabelDetection': {'Text': 'Customer',
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    {'Type': {'Text': 'OTHER', 'Confidence': 88.5},
     'LabelDetection': {'Text': 'Work',
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      'Confidence': 87.25090026855469},
     'ValueDetection': {'Text': '726-448-6720',
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     'ValueDetection': {'Text': 'Wednesday',
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            {'X': 0.8142945766448975, 'Y': 0.47615161538124084},
            {'X': 0.07752513885498047, 'Y': 0.47629812359809875}]},
          'Confidence': 74.46519470214844},
         'ValueDetection': {'Text': 'Delivery cost',
          'Geometry': {'BoundingBox': {'Width': 0.73675936460495,
            'Height': 0.024348895996809006,
            'Left': 0.07827553153038025,
            'Top': 0.6140632629394531},
           'Polygon': [{'X': 0.07827553153038025, 'Y': 0.6151914596557617},
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            {'X': 0.07840099185705185, 'Y': 0.6384121775627136}]},
          'Confidence': 99.94113159179688},
         'PageNumber': 1},
        {'Type': {'Text': 'PRICE', 'Confidence': 70.0},
         'LabelDetection': {'Text': '250',
          'Geometry': {'BoundingBox': {'Width': 0.07219740748405457,
            'Height': 0.020351318642497063,
            'Left': 0.8142009973526001,
            'Top': 0.45580029487609863},
           'Polygon': [{'X': 0.8142009973526001, 'Y': 0.45580044388771057},
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            {'X': 0.8142945766448975, 'Y': 0.47615161538124084}]},
          'Confidence': 74.48655700683594},
         'ValueDetection': {'Text': '200',
          'Geometry': {'BoundingBox': {'Width': 0.07219667732715607,
            'Height': 0.023167040199041367,
            'Left': 0.8149288296699524,
            'Top': 0.6139528751373291},
           'Polygon': [{'X': 0.8149288296699524, 'Y': 0.6140632629394531},
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            {'X': 0.887125551700592, 'Y': 0.636993408203125},
            {'X': 0.8150349259376526, 'Y': 0.6371198892593384}]},
          'Confidence': 99.97767639160156},
         'PageNumber': 1},
        {'Type': {'Text': 'OTHER', 'Confidence': 70.0},
         'LabelDetection': {'Text': '',
          'Geometry': {'BoundingBox': {'Width': 0.04210595414042473,
            'Height': 0.020337073132395744,
            'Left': 0.8863064646720886,
            'Top': 0.4558001756668091},
           'Polygon': [{'X': 0.8863064646720886, 'Y': 0.45580029487609863},
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            {'X': 0.886398434638977, 'Y': 0.47613725066185}]},
          'Confidence': 0.0},
         'ValueDetection': {'Text': '',
          'Geometry': {'BoundingBox': {'Width': 0.042110513895750046,
            'Height': 0.02310490794479847,
            'Left': 0.8870214223861694,
            'Top': 0.6138885021209717},
           'Polygon': [{'X': 0.8870214223861694, 'Y': 0.6139528751373291},
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            {'X': 0.887125551700592, 'Y': 0.636993408203125}]},
          'Confidence': 0.0},
         'PageNumber': 1},
        {'Type': {'Text': 'EXPENSE_ROW', 'Confidence': 99.99797058105469},
         'ValueDetection': {'Text': 'Delivery cost 200',
          'Geometry': {'BoundingBox': {'Width': 0.850856363773346,
            'Height': 0.02452363818883896,
            'Left': 0.07827553153038025,
            'Top': 0.6138885021209717},
           'Polygon': [{'X': 0.07827553153038025, 'Y': 0.6151914596557617},
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            {'X': 0.07840099185705185, 'Y': 0.6384121775627136}]},
          'Confidence': 99.95330810546875},
         'PageNumber': 1}]}]}]}],
 'ResponseMetadata': {'RequestId': '9f34f291-0003-4b28-bb07-7d23aff3c172',
  'HTTPStatusCode': 200,
  'HTTPHeaders': {'x-amzn-requestid': '9f34f291-0003-4b28-bb07-7d23aff3c172',
   'content-type': 'application/x-amz-json-1.1',
   'content-length': '47201',
   'date': 'Tue, 04 Oct 2022 14:06:16 GMT'},
  'RetryAttempts': 0}}

Finer-grain results

What if you only want a specific value in the invoice we processed?

document.expense_documents[0]["Phone"]

626-200-4890

document.expense_documents[0]["Phone"].raw_object

{'Text': '626-200-4890',
 'Geometry': {'BoundingBox': {'Width': 0.20498627424240112,
   'Height': 0.028003590181469917,
   'Left': 0.7725487947463989,
   'Top': 0.8846393823623657},
  'Polygon': [{'X': 0.7725487947463989, 'Y': 0.8854900598526001},
   {'X': 0.9774152040481567, 'Y': 0.8846393823623657},
   {'X': 0.9775350689888, 'Y': 0.9117385149002075},
   {'X': 0.7726749777793884, 'Y': 0.9126429557800293}]},
 'Confidence': 90.69705200195312}

The raw_object property is available on all Textractor objects

document.expense_documents[0].raw_object

{'ExpenseIndex': 1,
 'SummaryFields': [{'Type': {'Text': 'VENDOR_NAME',
    'Confidence': 84.23397827148438},
   'ValueDetection': {'Text': 'Royal',
    'Geometry': {'BoundingBox': {'Width': 0.33433136343955994,
      'Height': 0.1101887971162796,
      'Left': 0.16748881340026855,
      'Top': 0.10576257109642029},
     'Polygon': [{'X': 0.16748881340026855, 'Y': 0.10576257109642029},
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      {'X': 0.5018202066421509, 'Y': 0.2159513682126999},
      {'X': 0.168068990111351, 'Y': 0.2151809185743332}]},
    'Confidence': 83.68743133544922},
   'PageNumber': 1},
  {'Type': {'Text': 'OTHER', 'Confidence': 97.5},
   'LabelDetection': {'Text': '6 Mo',
    'Geometry': {'BoundingBox': {'Width': 0.037598639726638794,
      'Height': 0.010990276001393795,
      'Left': 0.4355943500995636,
      'Top': 0.9032412171363831},
     'Polygon': [{'X': 0.4355943500995636, 'Y': 0.903403103351593},
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      {'X': 0.4731929898262024, 'Y': 0.9140657186508179},
      {'X': 0.435648649930954, 'Y': 0.9142314791679382}]},
    'Confidence': 87.35102081298828},
   'ValueDetection': {'Text': '',
    'Geometry': {'BoundingBox': {'Width': 0.015726225450634956,
      'Height': 0.011696578934788704,
      'Left': 0.4170108139514923,
      'Top': 0.9020209908485413},
     'Polygon': [{'X': 0.4170108139514923, 'Y': 0.9020882844924927},
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      {'X': 0.41706937551498413, 'Y': 0.9137175679206848}]},
    'Confidence': 0.0},
   'PageNumber': 1},
  {'Type': {'Text': 'OTHER', 'Confidence': 96.5},
   'LabelDetection': {'Text': 'Permanent Wear',
    'Geometry': {'BoundingBox': {'Width': 0.12020225077867508,
      'Height': 0.010676798410713673,
      'Left': 0.09873555600643158,
      'Top': 0.4244430959224701},
     'Polygon': [{'X': 0.09873555600643158, 'Y': 0.4244430959224701},
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      {'X': 0.21893781423568726, 'Y': 0.4351198971271515},
      {'X': 0.09879285842180252, 'Y': 0.43509629368782043}]},
    'Confidence': 96.31539916992188},
   'ValueDetection': {'Text': '',
    'Geometry': {'BoundingBox': {'Width': 0.01464390754699707,
      'Height': 0.010014243423938751,
      'Left': 0.08258085697889328,
      'Top': 0.4239303171634674},
     'Polygon': [{'X': 0.08258085697889328, 'Y': 0.4239303171634674},
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      {'X': 0.08263488858938217, 'Y': 0.4339415431022644}]},
    'Confidence': 0.0},
   'PageNumber': 1},
  {'Type': {'Text': 'OTHER', 'Confidence': 96.0},
   'LabelDetection': {'Text': '12 Mo',
    'Geometry': {'BoundingBox': {'Width': 0.04926685988903046,
      'Height': 0.01091374084353447,
      'Left': 0.5017852783203125,
      'Top': 0.9013385772705078},
     'Polygon': [{'X': 0.5017852783203125, 'Y': 0.9015499949455261},
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      {'X': 0.5510521531105042, 'Y': 0.9120357632637024},
      {'X': 0.501838207244873, 'Y': 0.9122523069381714}]},
    'Confidence': 64.79031372070312},
   'ValueDetection': {'Text': '',
    'Geometry': {'BoundingBox': {'Width': 0.016481652855873108,
      'Height': 0.012412488460540771,
      'Left': 0.4822356104850769,
      'Top': 0.9004089832305908},
     'Polygon': [{'X': 0.4822356104850769, 'Y': 0.9004793167114258},
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      {'X': 0.4987172484397888, 'Y': 0.9127491116523743},
      {'X': 0.4822968542575836, 'Y': 0.9128214716911316}]},
    'Confidence': 0.0},
   'PageNumber': 1},
  {'Type': {'Text': 'OTHER', 'Confidence': 95.5},
   'LabelDetection': {'Text': 'Phone',
    'Geometry': {'BoundingBox': {'Width': 0.0415608249604702,
      'Height': 0.010550999082624912,
      'Left': 0.07793831080198288,
      'Top': 0.3300623297691345},
     'Polygon': [{'X': 0.07793831080198288, 'Y': 0.3300623297691345},
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      {'X': 0.07799505442380905, 'Y': 0.34056755900382996}]},
    'Confidence': 95.25244903564453},
   'ValueDetection': {'Text': '615-373-6883',
    'Geometry': {'BoundingBox': {'Width': 0.21608000993728638,
      'Height': 0.0200358759611845,
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      'Top': 0.3235915005207062},
     'Polygon': [{'X': 0.12751396000385284, 'Y': 0.3235915005207062},
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      {'X': 0.3435939848423004, 'Y': 0.34362736344337463},
      {'X': 0.12761986255645752, 'Y': 0.34339481592178345}]},
    'Confidence': 94.58015441894531},
   'PageNumber': 1},
  {'Type': {'Text': 'OTHER', 'Confidence': 95.5},
   'LabelDetection': {'Text': 'Loose Seams',
    'Geometry': {'BoundingBox': {'Width': 0.09154075384140015,
      'Height': 0.011243871413171291,
      'Left': 0.2909635007381439,
      'Top': 0.42348718643188477},
     'Polygon': [{'X': 0.2909635007381439, 'Y': 0.42348718643188477},
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    'Confidence': 95.17913055419922},
   'ValueDetection': {'Text': '',
    'Geometry': {'BoundingBox': {'Width': 0.014086964540183544,
      'Height': 0.009878362528979778,
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     'Polygon': [{'X': 0.27309396862983704, 'Y': 0.42410069704055786},
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    'Confidence': 0.0},
   'PageNumber': 1},
  {'Type': {'Text': 'OTHER', 'Confidence': 95.0},
   'LabelDetection': {'Text': 'Permanent Shading',
    'Geometry': {'BoundingBox': {'Width': 0.13924720883369446,
      'Height': 0.012071557343006134,
      'Left': 0.0985599234700203,
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      {'X': 0.0986248254776001, 'Y': 0.4508135914802551}]},
    'Confidence': 94.83258819580078},
   'ValueDetection': {'Text': '',
    'Geometry': {'BoundingBox': {'Width': 0.014641131274402142,
      'Height': 0.009928502142429352,
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      'Top': 0.43825197219848633},
     'Polygon': [{'X': 0.08259167522192001, 'Y': 0.43825197219848633},
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      {'X': 0.09723281115293503, 'Y': 0.4481804668903351},
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    'Confidence': 0.0},
   'PageNumber': 1},
  {'Type': {'Text': 'OTHER', 'Confidence': 95.0},
   'LabelDetection': {'Text': 'Allergy Concerns',
    'Geometry': {'BoundingBox': {'Width': 0.11545991897583008,
      'Height': 0.012368067167699337,
      'Left': 0.28926894068717957,
      'Top': 0.39615729451179504},
     'Polygon': [{'X': 0.28926894068717957, 'Y': 0.39615729451179504},
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      {'X': 0.28933262825012207, 'Y': 0.40847310423851013}]},
    'Confidence': 91.31575775146484},
   'ValueDetection': {'Text': '',
    'Geometry': {'BoundingBox': {'Width': 0.013979844748973846,
      'Height': 0.010146879591047764,
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     'Polygon': [{'X': 0.27254408597946167, 'Y': 0.39582815766334534},
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      {'X': 0.27259671688079834, 'Y': 0.40596839785575867}]},
    'Confidence': 0.0},
   'PageNumber': 1},
  {'Type': {'Text': 'OTHER', 'Confidence': 93.0},
   'LabelDetection': {'Text': 'Excessive Wear',
    'Geometry': {'BoundingBox': {'Width': 0.10464227199554443,
      'Height': 0.01053692027926445,
      'Left': 0.0990402102470398,
      'Top': 0.4101735055446625},
     'Polygon': [{'X': 0.0990402102470398, 'Y': 0.4101735055446625},
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      {'X': 0.09909669309854507, 'Y': 0.4206753969192505}]},
    'Confidence': 92.89436340332031},
   'ValueDetection': {'Text': '',
    'Geometry': {'BoundingBox': {'Width': 0.01583889126777649,
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     'Polygon': [{'X': 0.08204009383916855, 'Y': 0.4095456898212433},
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      {'X': 0.08209425956010818, 'Y': 0.41958194971084595}]},
    'Confidence': 0.0},
   'PageNumber': 1},
  {'Type': {'Text': 'OTHER', 'Confidence': 92.5},
   'LabelDetection': {'Text': 'Time',
    'Geometry': {'BoundingBox': {'Width': 0.02681553363800049,
      'Height': 0.010385806672275066,
      'Left': 0.659330427646637,
      'Top': 0.3567666709423065},
     'Polygon': [{'X': 0.659330427646637, 'Y': 0.3567666709423065},
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      {'X': 0.6593798398971558, 'Y': 0.3671296536922455}]},
    'Confidence': 92.33248901367188},
   'ValueDetection': {'Text': '12.30 P.M.',
    'Geometry': {'BoundingBox': {'Width': 0.1608247458934784,
      'Height': 0.019669130444526672,
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     'Polygon': [{'X': 0.6893413662910461, 'Y': 0.350131094455719},
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    'Confidence': 91.03388214111328},
   'PageNumber': 1},
  {'Type': {'Text': 'OTHER', 'Confidence': 92.0},
   'LabelDetection': {'Text': 'Cleaning Tech',
    'Geometry': {'BoundingBox': {'Width': 0.11247267574071884,
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    'Confidence': 90.90103912353516},
   'ValueDetection': {'Text': 'JOHN LEWIS',
    'Geometry': {'BoundingBox': {'Width': 0.1771693378686905,
      'Height': 0.026054145768284798,
      'Left': 0.6595302224159241,
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    'Confidence': 91.7969970703125},
   'PageNumber': 1},
  {'Type': {'Text': 'OTHER', 'Confidence': 91.0},
   'LabelDetection': {'Text': 'Cellular',
    'Geometry': {'BoundingBox': {'Width': 0.045506563037633896,
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    'Confidence': 90.93302154541016},
   'ValueDetection': {'Text': '683-426-2200',
    'Geometry': {'BoundingBox': {'Width': 0.21450038254261017,
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    'Confidence': 90.27494812011719},
   'PageNumber': 1},
  {'Type': {'Text': 'OTHER', 'Confidence': 90.5},
   'LabelDetection': {'Text': 'Customer',
    'Geometry': {'BoundingBox': {'Width': 0.08403262495994568,
      'Height': 0.014190987683832645,
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      {'X': 0.06905244290828705, 'Y': 0.8971186280250549}]},
    'Confidence': 90.06220245361328},
   'ValueDetection': {'Text': 'ELIZABETH CAMPBELL',
    'Geometry': {'BoundingBox': {'Width': 0.29250043630599976,
      'Height': 0.022797495126724243,
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    'Confidence': 88.27864837646484},
   'PageNumber': 1},
  {'Type': {'Text': 'OTHER', 'Confidence': 89.0},
   'LabelDetection': {'Text': 'Laminate Floor Concerns',
    'Geometry': {'BoundingBox': {'Width': 0.17273929715156555,
      'Height': 0.010896960273385048,
      'Left': 0.28804612159729004,
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       'ValueDetection': {'Text': 'Tile cleaning repair',
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        'Confidence': 99.86579132080078},
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      {'Type': {'Text': 'PRICE', 'Confidence': 70.0},
       'LabelDetection': {'Text': '250',
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        'Confidence': 74.48655700683594},
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        'Confidence': 99.98596954345703},
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      {'Type': {'Text': 'OTHER', 'Confidence': 70.0},
       'LabelDetection': {'Text': '',
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        'Confidence': 0.0},
       'ValueDetection': {'Text': '',
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        'Confidence': 0.0},
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       'ValueDetection': {'Text': 'Tile cleaning repair 200',
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        'Confidence': 99.89582061767578},
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    {'LineItemExpenseFields': [{'Type': {'Text': 'ITEM', 'Confidence': 70.0},
       'LabelDetection': {'Text': 'Carpet Cleaning',
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        'Confidence': 74.46519470214844},
       'ValueDetection': {'Text': 'Delivery cost',
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        'Confidence': 99.94113159179688},
       'PageNumber': 1},
      {'Type': {'Text': 'PRICE', 'Confidence': 70.0},
       'LabelDetection': {'Text': '250',
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        'Confidence': 74.48655700683594},
       'ValueDetection': {'Text': '200',
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        'Confidence': 99.97767639160156},
       'PageNumber': 1},
      {'Type': {'Text': 'OTHER', 'Confidence': 70.0},
       'LabelDetection': {'Text': '',
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         'Polygon': [{'X': 0.8863064646720886, 'Y': 0.45580029487609863},
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        'Confidence': 0.0},
       'ValueDetection': {'Text': '',
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        'Confidence': 0.0},
       'PageNumber': 1},
      {'Type': {'Text': 'EXPENSE_ROW', 'Confidence': 99.99797058105469},
       'ValueDetection': {'Text': 'Delivery cost 200',
        'Geometry': {'BoundingBox': {'Width': 0.850856363773346,
          'Height': 0.02452363818883896,
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        'Confidence': 99.95330810546875},
       'PageNumber': 1}]}]}]}

Conclusion

With Textractor, we strived to offer a tool that does most of the work, but also one that gets out of the way when you know better. If you find a Textractor object that does not have a raw_object property, open an issue on our official GitHub repository.