Nearly all business processes today begin, include or end with a document. Most companies are sitting on the document goldmine. Thinking of which some are PDFs, emails, customer feedback, patents, contracts, technical documents, sensitive documents, HR files and the list goes on. These documents are only going to grow with time. Making sense of each document is difficult since a lot of these documents are unstructured which can be very time consuming process.
What is Unstructured Data?
Unstructured Data comprises of 80% of enterprise data.This data is free form of text which is either Human generated (through emails, files, videos, physical documents) and machine generated (satellite imagery or sensor data). Extracting data from documents is expensive and difficult to harness since its more like human language and does not have any predefined format.
Document processing has become increasingly complex. This is because of large volume of data and its diversity. Diversity meaning different document types and formats. Diversity is increasing continuously because of government regulations and changes in business types relationships and entity linkages with documents over a period of time.Understanding the semantic depth of document is also important to unlock insights within businesses.
Document processing is quite challenging. It includes –
- Various Documents formats and shapes
- Manual processes and cost of error
- Bad Data
- Long processing times and delays
- Insufficient data accuracy
- Multiple workflows
- Access Management
Businesses are thus affected by high costs, lost revenue and missed opportunities. That’s where Google Cloud Document AI comes in picture.
What is Document AI?
Google Cloud Document AI service is a document understanding solution which allows you to process documents and parse out their content in structured or machine readable data. Examples of documents may include :
- Driving License or Passport
- Bank Statement
- Income Declarations
- Medication Form
- Tax Documents
Document AI extracts information from unstructured/structured documents. This can enable businesses to make better decisions such as analysing customer feedback, processing invoices or reduced mortgage processing times.
Document AI is build upon output from components of other Machine Learning areas. Google Vision and Natural Language Processing provides the foundation for building Document Knowledge Base.
Document AI building blocks
The three building blocks of Document AI are:
- General Document AI – Applying OCR and text processing services to extract structure/content from any business document.
- Custom Document AI (AutoML) – Create private models and train models for your docs, forms and use-cases. Train custom models on your content, to identify domain specific content tuned on your own specific training data.
- Specialized Document AI – Prebuild high quality models optimized for the world’s most important businesses. You can use Google’s pretrained models to get out of the box extraction and classification for some of most common document types in the world.
Below images shows the different processors available in Document AI.
Applications of Document AI
- Retail – Use in-store feedback and online reviews to improve VOC analytics and demand forecasting.
- Financial – Ensure applications with hundreds of documents are complete, accurate and compliant. Cut processing time from days to hours.
- Healthcare – Better management of medical bills and analysis.
- Industrial – Expenditure analysis using different type of invoices.