Documents

Standard output for documents lets you set the granularity of response you're interested in as well as establishing output format and text format in the output. Below are some of the outputs you can enable.

Response Granularity

Response granularity determines what kind of response you want to recieve from document text extraction. Each level of granularity gives you more and more seperated responses, with page providing all of the text extracted together, and word providing each word as a seperate response. The available granularity levels are:

Output Settings

Output settings determine the way your downloaded results will be structured. The options for output settings are:

Text Format

Text format determines the different kinds of texts that will be provided via various extraction operations. You can select any number of the following options for your text format.

Bounding Boxes and Generative Fields

For Documents, there are two response options that change their output based on the selected granularity. These are Bounding Boxes, and Generative Fields. Selecting Bounding Boxes will provide a visual outline of the element or word you click on in the console response dropdown. This lets you track down particular elements of your response more easily. Bounding Boxes are returned in your JSON as the coordinates of the four corners of the box.

When you select Generative Fields, you are generated a summary of the document, both a 10 word and 250 word version. Then, if you select elements as a response granularity, you generate a descriptive caption of each figure detected in the document. Figures include things like charts, graphs, and images.

Bedrock Data Automation document response

This section focuses on the different response objects you receive from running the API operation InvokeDataAutomation on a document file. Below we'll break down each section of the response object and then see a full, populated response for an example document. The first section we'll receive is metadata.

"metadata":{
   "logical_subdocument_id":"XXXX-XXXX-XXXX-XXXX",
   "semantic_modality":"DOCUMENT",
   "s3_bucket":"bucket",
   "s3_prefix":"prefix"
},

The first section above provides an overview of the metadata associated with the document. Along with the S3 information, this section also informs you which modality was selected for your response.

"document":{
   "representation":{
      "text":"document text",
      "html":"document title document content",
      "markdown":"# text"
   },
   "description":"document text",
   "summary":"summary text",
   "statistics":{
      "element_count":5,
      "table_count":1,
      "figure_count":1,
      "word_count":1000,
      "line_count":32
   }
},

The above section provides document level granularity information. The description and summary sections are the generated fields based on the document The representation section provides the actual content of the document with various formatting styles. Finally statistics contains information on the actual content of the document, such as how many semantic elements there are, how many figures, words, lines, etc.

This is the information for a table entity. In addition to location information, the different formats of the text, tables, and reading order, they specifically return csv information and cropped images of the table in S3 buckets. The CSV information shows the different headers, footers, and titles. The images will be routed to the s3 bucket of the prefix set in the InvokeDataAutomationAsync request

{
   "id":"entity_id",
   "type":"TEXT",
   "representation":{
      "text":"document text",
      "html":"document title document content",
      "markdown":"# text"
   },
   "reading_order":2,
   "page_indices":[
      0
   ],
   "locations":[
      {
         "page_index":0,
         "bounding_box":{
            "left":0.0,
            "top":0.0,
            "width":0.05,
            "height":0.5
         }
      }
   ],
   "sub_type":"TITLE/SECTION_TITLE/HEADER/FOOTER/PARAGRAPH/LIST/PAGE_NUMBER"
},

This is the entity used for text within a document, indicated by the TYPE line in the response. Again representation shows the text in different formats. reading_order shows when a reader would logically see the text. This is a semantic ordering based on associated keys and values. For example, it associates titles of paragraphs with their respective paragraph in reading order. page_indices tells you which pages the text is on. Next is location information, with a provided text bounding box if it was enabled in response. Finally, we have the entity subtype. This subtype provides more detailed information on what kind of text is being detected. For a complete list of subtypes see the API Reference.

{
   "id":"entity_id",
   "type":"TABLE",
   "representation":{
      "html":"table.../table",
      "markdown":"| header | ...",
      "text":"header \t header",
      "csv":"header, header, header\n..."
   },
   "csv_s3_uri":"s3://",
   "headers":[
      "date",
      "amount",
      "description",
      "total"
   ],
   "reading_order":3,
   "title":"Title of the table",
   "footers":[
      "the footers of the table"
   ],
   "crop_images":[
      "s3://bucket/prefix.png",
      "s3://bucket/prefix.png"
   ],
   "page_indices":[
      0,
      1
   ],
   "locations":[
      {
         "page_index":0,
         "bounding_box":{
            "left":0,
            "top":0,
            "width":1,
            "height":1
         }
      },
      {
         "page_index":1,
         "bounding_box":{
            "left":0,
            "top":0,
            "width":1,
            "height":1
         }
      }
   ]
},

This is the information for a table entity. In addition to location information, the different formats of the text, tables, and reading order, they specifically return csv information and cropped images of the table in S3 buckets. The CSV information shows the different headers, footers, and titles. The images will be routed to the s3 bucket of the prefix set in the InvokeDataAutomation request.

{

   "id":"entity_id",

   "type":"FIGURE",

   "summary":"",

   "representation":{

      "text":"document text",

      "html":"document title document content",

      "markdown":"# text"

   },

   "crop_images":[

      "s3://bucket/prefix.png",

      "s3://bucket/prefix.png"

   ],

   "locations":[

      {

         "page_index":0,

         "bounding_box":{

            "left":0,

            "top":0,

            "width":1,

            "height":1

         }

      }

   ],

   "sub_type":"CHART",

   "title":"figure title",

   "rai_flag":"APPROVED/REDACTED/REJECTED",

   "reading_order":1,

   "page_indices":[

      0

   ]

}
,

​

This is the entity used for figures such as in document graphs and charts. Similar to tables, these figures will be cropped and images sent to the s3 bucket set in your prefix. Additionally, you'll receive a sub_type and a figure title response for the title text and an indication on what kind of figure it is.

"pages":[
   {
      "id":"page_id",
      "page_index":0,
      "detected_page_number":1,
      "representation":{
         "text":"document text",
         "html":"document title document content",
         "markdown":"# text"
      },
      "statistics":{
         "element_count":5,
         "table_count":1,
         "figure_count":1,
         "word_count":1000,
         "line_count":32
      },
      "asset_metadata":{
         "rectified_image":"s3://bucket/prefix.png",
         "rectified_image_width_pixels":1700,
         "rectified_image_height_pixels":2200
      }
   }
],

The last of the entities we extract through standard output is Pages. Pages are the same as Text entities, but additionally contain page numbers, for which detected page number is on the page.

"text_lines":[
   {
      "id":"line_id",
      "text":"line text",
      "reading_order":1,
      "page_index":0,
      "locations":{
         "page_index":0,
         "bounding_box":{
            "left":0,
            "top":0,
            "width":1,
            "height":1
         }
      }
   }
],

"text_words":[
   {
      "id":"word_id",
      "text":"word text",
      "line_id":"line_id",
      "reading_order":1,
      "page_index":0,
      "locations":{
         "page_index":0,
         "bounding_box":{
            "left":0,
            "top":0,
            "width":1,
            "height":1
         }
      }
   }
]

These final two elements are for individual text portions. Word level granularity returns a response for each word, while default output reports only lines of text.

BDA Document Processing Restrictions

BDA supports documents in PDF, JPEG, and PNG file formates. Documents must be less than 200 MB to be processed by the console, or 500 MB when procesed by the API. Single documents cannot exceed 20 pages, although with document splitting enabled files with up to 1500 pages may be submitted.