The immediate availability of structured data from a variety of unstructured documents in the right place is critical for companies. Intelligent Document Processing (IDP) enables exactly that and is one of the key components in automating complex business processes. For this, IDP makes use of various technologies, some of them based on Artificial Intelligence (AI), and combines all these relevant ones into one system.
This is a brief overview of the essential technologies behind IDP.
Table of contents
- Optical Character Recognition (OCR)
- Machine Learning (ML)
- Deep Learning
- Natural Language Processing (NLP)
- Intelligent Document Processing (IDP)
Optical Character Recognition (OCR)
Optical Character Recognition (OCR) is the technology behind converting text and information from images, physical documents (scans) and PDFs into machine-readable data formats. Capturing the documents and extracting them into structured data allows them to be routed to the correct system for further processing or is suitable for digital archiving of documents and data.
Advantages with OCR:
- reduces manual data entry
- digitizes and secures data
- reduces costs and saves time
Limits of OCR:
- Traditional OCR systems are associated with high project and licensing costs
- Data cannot be extracted from complex documents that deviate from templates
- OCR systems are very inflexible
One of the key problems with traditional OCR software is the lack of insight into the content of documents. Or, to put it another way, simply being able to extract template-based documents is no longer enough for businesses. For end-to-end automations, they rely on capturing and understanding context from within the documents.
Only when OCR is combined with advanced AI technologies can the limits of OCR be solved.
Machine Learning (ML)
Machine Learning-based OCR systems offer a solution to the limitations of conventional OCR software. Machine Learning (ML) is one of the key components of IDP. By incorporating ML, the IDP system learns from data and can make predictions based on it.
The most commonly used ML algorithms for IDP are neural networks and support vector machines. While neural networks are used for pattern recognition, support vector machines can be used to make predictions about what kind of content is contained in a particular document.
ML algorithms are trained and validated with data sets. Model structures are optimized and adjusted if necessary. If the results are not satisfactory, the ML algorithms must be optimized with new training sets.
Advantages with Machine Learning:
- Helps IDP systems process complex documents more efficiently
- High accuracy of extracted data
Deep Learning
Advances in Deep Learning, such as the advent of transformer architectures, have rapidly accelerated the learning curve in handwritten text recognition. To figure out what letter, number, or character in the docment is, the technology pulls pixels together and identifies them.
With the Deep Learning approach, neural networks are fed a large data set and trained. Deep Learning technologies traverse the immense dataset, identify patterns and decide on the basis of these for accurate extraction of the handwritten text. With the combination of different Deep Learning architectures, a wide range of handwriting can thus be covered.
Advantages with Deep Learning:
- recognizes great differences in handwriting
Natural Language Processing (NLP)
For documents with pure text data, such as contracts or letters, several processing stages are required. For interpreting and analyzing such text data, Natural Language Processing (NLP) adds another component. NLP helps OCR technologies to recognize relevant concepts in the text, which is highly useful for ML algorithms.
Advantages with Natural Language Processing:
- Documents with plain text, for example letters or contracts, can be extracted
Intelligent Document Processing (IDP)
Combining OCR with advanced AI technologies such as Machine Learning, Deep Learning and Natural Language Processing in one system enables Intelligent Document Processing (IDP). By uniting them, IDP is capable of mimicking cognitive abilities, i.e. capturing documents correctly, classifying them correctly and extracting all relevant data from them. We are talking here about complex, unstructured documents, such as those that have to be processed every day in companies.
The relevant data read out is automatically forwarded to the correct workflows for further processing. Thus IDP is an integral technology enabling hyperautomation across a company through the processing of structured, semistructured, and unstructured documents.
Some benefits of IDP:
- IDP understands the context of unstructured data, just like a human, and is predestined for unstructured documents
- IDP requires no templates and therefore much less configuration
- IDP is an automation accelerator as it captures, classifies and extracts all incoming documents
- IDP delivers high quality data for further processing
- IDP exhibits high efficiency and accuracy in document extraction, thanks to the advanced AI technologies mentioned above
- IDP is capable of learning on the basis of data
- IDP is very flexible and highly scalable
