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Introduction

In this example, we’ll use DocParse to extract data from an NTSB report. We’ll go through the important code snippets below to see what’s going on. For the purposes of this tutorial, we focus on page 2 of the document, given below.

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Getting Parsed Document

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Making a Call to DocParse

text_demo.py
with open(file_name, 'rb') as file:
  ## Make a call to the partitioning service and set extract_images to true.
  partitioned_file = partition_file(file, aryn_api_key, extract_images=True, extract_table_structure=True, use_ocr=False, selected_pages=[2])

We use the partition_file function from aryn_sdk.partition to extract text and images from the document. The aryn_api_key is your API key from Aryn. In this example, we set use_ocr to False to avoid OCR and use embedded text, and set extract_images and extract_table_structure to True to extract images and tables. We use selected_pages=[2] to focus on page 2.

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Viewing the JSON Output

output.json
{'status': ['Incremental status will be shown here during execution.',
  "Until you get a line that matches '  ]\n', you can convert the partial",
  'output to a json document by appending \'""]}\' to the partial output.',
  '',
  'T+   0.00: Server version managed-service-22-model-1-4 Model version 1.4',
  'T+   0.00: Received request with aryn_call_id=REDACTED',
  'T+   0.00: Waiting for scheduling',
  'T+   0.01: Preprocessing document',
  'T+   0.01: Done preprocessing document',
  'T+   0.51: Completed work on page 2',
  ''],
 'elements': [{'type': 'Image',
   'bbox': [0.0873676793715533,
    0.08911955399946733,
    0.8548237161075367,
    0.5357984508167614],
   'properties': {'score': 0.873895525932312,
    'image_size': [1324, 1003],
    'image_mode': 'RGB',
    'page_number': 2},
   'binary_representation': ...
 }

Above, you can see the JSON output from the call to Aryn DocParse. The output is a JSON object with a status field that shows the status of the call and an elements field that contains a list of elements extracted from the document. The call ID has been redacted for privacy reasons.

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Examining Individual Elements

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Extracting an Image from the Document

image.json
{ 
  type': 'Image',
 'bbox': [0.0873676793715533,
  0.08911955399946733,
  0.8548237161075367,
  0.5357984508167614],
 'properties': {'score': 0.873895525932312,
  'image_size': [1324, 1003],
  'image_mode': 'RGB',
  'page_number': 2},
 'binary_representation': ...
}

The first element we see is an image. We get back a bounding box and a binary representation of the image, which we can use to display the image. The bounding boxes are given in the format [x1, y1, x2, y2], where (x1, y1) is the top-left corner and (x2, y2) is the bottom-right corner of the bounding box.

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Extracting Captions from the Document

caption.json

{'type': 'Caption',
 'bbox': [0.08805406458237591,
  0.5466218150745739,
  0.8386106244255515,
  0.5634380548650568],
 'properties': {'score': 0.6033732891082764,
  'page_number': 2},
 'text_representation': 'Figure 1. Accident airplane as it came to rest (Source: Federal Aviation Administration)\n'}

The second element we see is a caption. We get back a bounding box and the text content of the caption.

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Extracting Text from the Document

text.json
{'type': 'Text',
 'bbox': [0.08857761158662684,
  0.5770943936434659,
  0.5373964197495404,
  0.5928012917258523],
 'properties': {'score': 0.589328944683075,
  'page_number': 2},
 'text_representation': 'The wreckage was retained for further examination. \n'}

The third element we see is a text element. We get back a bounding box and the text content of the element.

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Extracting a Table from the Document

table.json

{ 
 'type': 'table',
 'bbox': [0.09319061279296875,
  0.6873569003018466,
  0.9122876694623162,
  0.8174545010653409],
 'properties': {'score': 0.8106631636619568,
  'title': None,
  'columns': None,
  'rows': None,
  'page_number': 2},
 'text_representation': None,
 'table': {'cells': [{'content': 'Aircraft Make:',
    'rows': [0],
    'cols': [0],
    'is_header': True,
    'bbox': {'x1': 0.09705882369281045,
     'y1': 0.6968232236868688,
     'x2': 0.27310294133986934,
     'y2': 0.7097020113257576},
    'properties': {}},
   {'content': 'MARC JONES',
    'rows': [0],
    'cols': [1],
    'is_header': True,
    'bbox': {'x1': 0.31151960800653594,
     'y1': 0.6968232236868688,
     'x2': 0.43984150343137257,
     'y2': 0.7097020113257576},
    'properties': {}},...
}

The fourth element we see is an table. We get back a bounding box and the table structure, which includes the cells of the table and their properties.

Walking through the first cell above, we see that there are 6 attributes: content, rows, cols, is_header, bbox, and properties. The content attribute contains the text content of the cell, the rows attribute contains the row index of the cell, the cols attribute contains the column index of the cell, the is_header attribute indicates whether the cell is a header cell and is optional, the bbox attribute contains the bounding box of the cell, and the properties attribute contains additional properties of the cell.

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Displaying the Table

Here we display the table in clean markdown format below. We clean the column headers to make them a separate row in the table.

display_table.py
import pandas as pd
pandas = tables_to_pandas(partitioned_file)
# Let's display the pandas DataFrame
table = pandas[4][1]
table = pd.concat([pd.DataFrame([table.columns], columns=table.columns), table], ignore_index=True)

table.columns = ['' for col in table.columns] # Optionally reset the column headers

table

The output is given below:

0Aircraft Make:MARC JONESRegistration:N512P
1Model/Series:PITTS MODEL 12Aircraft Category:Airplane
2Amateur Built:
3Operator:M12 AVIATION LLCOperating Certificate(s)None
Held:
4Operator Designator Code:

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Output Label OptionsTutorial: Table Extraction