Today, many enterprises extract data from scanned documents, such as PDF’s, tables and forms,
through manual data entry (that is slow, expensive and prone to errors), or through simple OCR
software that requires manual configuration which needs to be updated each time the form changes
to be usable. To overcome these manual processes, Deep learning based approaches have been
developed to instantly read and process any type of document, accurately extracting printed
text, handwriting, forms, tables and other data without the need for any manual effort or custom
code.
While there are many purpose-built third party softwares available, cloud providers have
democratized the OCR capabilities. The popular cloud services include Amazon Textract, Google
Vision or Microsoft Azure’s OCR Service. Many enterprises have adopted these services to unlock
data out of PDFs or Image documents. So, we recommend customers to not waste cycles and valuable
data science effort on building OCR systems.
When your organization processes a variety of documents, you sometimes need to
extract entities
from unstructured text in the documents. A contract document, for example, can have paragraphs
of text where names and other contract terms are listed in the paragraph of text instead of as a
key/value or form structure. Amazon Comprehend is a natural language processing (NLP) service
that can extract key phrases, places, names, organizations, events, sentiment from unstructured
text, and more. With custom entity recognition, you can identify new entity types not supported
as one of the preset generic entity types. This allows you to extract business-specific entities
to address your needs.
The custom entity recognition models require high quality labeled data for
training. Performing
annotations on blobs of text makes it very hard for understanding the context of a document.
Let’s say in the document below, we need to extract key skills and experience. You can see that
annotating it in original PDF appears lot easier and accurate than OCRed blob of text.
Formatted PDF
Extracted Text
So, how do I go about achieving better PDF annotation. Before diving deep, bit
about labeling
tools. While there are many labeling tools in the market, Amazon SageMaker GroundTruth offers
lots of flexibility to create custom annotation UI and unlike other tools, it is entirely pay as
you go means we can do a lot of experimentation without lock in.
We demonstrate how you can use Objectways developed PDF annotation tool label
PDF documents for
Named Entity Recognition(NER) labeling. The annotation tool provides labeling entities,
relationships among entities, overlapping entities, document classification along with a custom
notes field all in a single annotation UI. The tool is really easy to configure and compatible
with SageMaker GroundTruth. It also supports multi-page annotation. The input to the annotation
tool is searchable PDF which can be easily created using a freely available utility on GitHub.
The utility uses Amazon Textract to OCR and then creates a searchable PDF as the output.
