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Introduction

In this example, we’ll use DocParse to extract an image from a battery manual. We’ll go through the important code snippets below to see what’s going on.

Let’s focus on the following code that makes a call to Aryn DocParse API to extract the image from the PDF.:

add_document.py
import aryn_sdk
from aryn_sdk.partition import partition_file, tables_to_pandas
import pandas as pd
from io import BytesIO

file = open('my-document.pdf', 'rb')
aryn_api_key = 'YOUR-KEY-HERE'

## Make a call to Aryn DocParse 
## param use_ocr (boolean): extract text using an OCR model instead of extracting embedded text in PDF. default: False
## param extract_images (boolean):  extract image contents. default: False
## returns: JSON object with elements representing information inside the PDF
partitioned_file = partition_file(file, aryn_api_key, extract_images=True, use_ocr=True)

If you inspect the partitioned_file variable, you’ll notice that it’s a large JSON object with details about all the components in the PDF (checkout this page to understand the schema of the returned JSON object in detail). Below, we highlight the Image element that contains the information about some of the images in the page:

docparse_output.json
[
  {
    "type": "Section-header",
    "bbox": [
      0.06470742618336398,
      0.08396875554865056,
      0.3483343505859375,
      0.1039327656139027
    ],
    "properties": {
      "score": 0.7253036499023438,
      "page_number": 1
    },
    "text_representation": "Make AC Power Connections\n"
  },
  {
    "type": "Image",
    "bbox": [
      0.3593270694508272,
      0.10833765896883878,
      0.6269251924402574,
      0.42288088711825284
    ],
    "properties": {
      "score": 0.7996300458908081,
      "image_size": [
        475,
        712
      ],
      "image_mode": "RGB",
      "image_format": null,
      "page_number": 1
    },
    "text_representation": "",
    "binary_representation": "AAAAAA.."
    },
]

In particular let’s look at the element which highlights the Image that has been detected.

image_output.json
{
    "type": "Image",
    "bbox": [
      0.3593270694508272,
      0.10833765896883878,
      0.6269251924402574,
      0.42288088711825284
    ],
    "properties": {
      "score": 0.7996300458908081,
      "image_size": [
        475,
        712
      ],
      "image_mode": "RGB",
      "image_format": null,
      "page_number": 1
    },
    "text_representation": "",
    "binary_representation": "AAAAAA.."
    }

​
Extracting the Image

This JSON object represents one of the images in the PDF. You’ll notice that the image’s binary representation, its bounding box (which indicates the coordinates of the image in the PDF), and certain other properties (image_mode, image_size etc.) are returned back. You can then process this JSON however you’d like for further analysis. In the notebook, we use the Pillow Image module from python to display the extracted image on its own.

image_output.py
## extract all the images from the JSON and print out the JSON representation of the first image
images = [e for e in partitioned_file['elements'] if e['type'] == 'Image']
first_image = images[0]

## read in the image and display it
image_width = first_image['properties']['image_size'][0]
image_height = first_image['properties']['image_size'][1]
image_mode = first_image['properties']['image_mode']
image = Image.frombytes(image_mode,  (image_width, image_height), base64.b64decode(first_image['binary_representation']))

#display the image
image

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Output Image

Here’s the output image after extraction. As you can see, the image has been successfully extracted from the PDF with clarity.

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