The age of digitization and automation is picking up pace. Artificial intelligence is touching many business and industrial processes, addressing several business expectations including efficiency, productivity, and ROI. One of the processes where the intelligent automation model seems set to drive massive value in enterprises is processing the unstructured documents – how information in documents is extracted, stored, processed, and managed. This new technique that possesses cognitive capabilities beyond mere rule-based automation is what we refer to as ‘Intelligent Document Processing‘.[SD1]
At the core of modern business processes lies the challenge of extracting information from a document and taking a specific action. In the quest for consistency, repeatability, and efficiency, businesses have created standard workflows for most tasks.
Without the right intelligent automation in place, documents have to be reviewed manually to extract the relevant information that can spark the next action dictated by the workflow. The manual process offers little consistency, timeliness, and efficiency – which eventually translates to errors, rework, and additional cost.
Challenges in all-human interventions
Human potential is the driving force behind creations, innovations, and connection. But mundane processes are the crucial battleground for human intervention and the question of how much value they can bring. Honestly, humans often need stimulation for routine tasks. However, it’s also in such mundane, repetitive, and everyday tasks that humans also commit errors, delay, and fail to deliver consistency.
As it happens, routine is something that can be easily automated for better outcomes.
Consider a process like invoice processing in an enterprise. Large enterprises will have hundreds of suppliers generating thousands of invoices. These invoices have to be manually read and interpreted to extract the relevant information required to kick off the payments process. The workflow of approvals, budget release, payment, and accounting acknowledgment can flow smoothly only after the manual first step. Given the sheer volume of invoices, think of the productivity improvement if the manual process of data entry gets automated?
Intelligent Document Processing with OCR (Optical Character Recognition)
Automation demands addressing semi-structured and unstructured data. Handling structured data is largely a solved problem, the challenge arises for unstructured data. Lets consider the common industry-wide case of invoice processing. Think of the different layouts, varying text, descriptions and standards that different vendors follow. How to automate that?
What adds human-like intelligence to computing capabilities is the integration of ML (Machine Learning). This is the key to enhancing more cognitive capabilities and addressing the complexity of the processes being automated. This is also where intelligent OCR (Optical Character Recognition) technology helps in document definition, document identification, data extraction and classification, and learning.
OCR looks at the digital form of images and texts to identify underlying patterns and converts them into text. However, just OCR is not enough. Once you extract all the text from the document, it is essential to extract only the text/ fields that are of most relevance. Another recent technique that uses ‘template-based approach‘ is gaining popularity due to its simplicity in integration. However, the user-efforts involved in such template-based solutions are quite resource and time depleting.
‘Intelligent Document Processing’ is one that deploys ML / Deep Learning Models, then steps-in to overcome these very limitations. Using various ML techniques, one can train the machine to predict the parts of the entire text into relevant fields. Due to the diverse types of documents (contracts, invoices, resumes, payment advice, purchase orders etc), it is important to classify, validate and constantly learn from user inputs.
Business expectations in Intelligent Document Processing
When it comes to digitally transforming the enterprise processes with intelligent document processing, here’s what enterprises expect:
Processing more complex semi-structured and unstructured documents
Ability to process hand-written documents
Ability to define, extract, and validate relevant data
Minimum manual effort
Rule-based as well as self-learning algorithm
Cognitive capability
Essential Components of Intelligent Automation
Here’s how intelligent automation of enterprise processes could pan out:
Defining the Documents – Defining the document is initially a complex process where the machine learns to discriminate between different types of semi-structured or unstructured data. This phase teaches machines to consider all the elements of the documents likely to be processed. Some samples in the beginning help teach the ML models. The performance and polish will improve as more documents are used for training. For instance, you can feed the most universal standard of your business invoice format for quick output.
Classification – Classification seems similar to the first step but it involves reading a scanned document and matching it with an in-built template in the system. Hence, if you have defined templates X and Y already, the solution will read and classify the input document as X or Y.
Extraction – Extraction stands for identifying the relevant data elements individually from the document. For every specific use case, the specific rules are defined to extract the information such as objects like signatures, titles, checkboxes, buttons, bar codes, characters, etc. The extraction happens in a more traditional manner i.e. by using text extraction templates. This is a completely rules based method of extraction.
Validation – Validation is all about confirming the data items extracted by the system especially for complex items such as hand-written text, human signatures and mapping the extracted text to specific fields. This potential unit of intelligent automation software comes handy for improving machine confidence in future processes.
The evolution in the technology is visible even as we consider the process outlined here. This process, as laid out here, involves a significant manual effort at the beginning to define the templates and lay out the rules. The value of automating this process can be enhanced when the document processing becomes independent of templates and intuitive so that it’s not restricted by rules. That’s the emerging standard in intelligent automation. ML presents a more sophisticated, cost effective and scalable method.
Business benefits of implementing Machine Learning in unstructured documents management
Enterprises adopting intelligent document processing solutions for automating mundane tasks will experience the following benefits:
Simplification of massive process complexity
Reduction in human errors
Improved ROI
Enhanced productivity
Faster processing speed and efficiency
Better informed decisions
Conclusion
Automation is well-placed to become the norm across businesses for handling mundane tasks. However, the next wave of process automation would revolve around amping up the processing of semi-structured and unstructured documents. Processing invoices, resumes, legal contracts, and other documents manually would be inefficient, non-scalable, complex, and might lead to errors. Intelligent Document Processing models built using OCR, AI, and ML to automate these routine processes will improve efficiency, effectiveness, accuracy, and consistency. That will no doubt, drive the next wave of automation in enterprise processes.
Comments