We have strong experience implementing OCR solution along with RPA for a large insurance provider that was dealing with 100,000 semi-structured and unstructured claims documents yearly. The automation helped save 85,000 work hours and $1.2MM with a payback in 2 years. We also successfully implemented OCR along with RPA to extract highly sensitive land registration data with 90-95% accuracy for a large land registration company with volumes of over 150,000/year. The automation allowed 24 FTE’s to focus on high value customer issues while the BOT’s took care of all the validations of land registrations documents.
After we fetched the data, it was essential to understand the type of document and the format with which they were saved in our systems, as sometimes, we receive data from different sources in various file formats such as PDF, PNG, and JPG. Not just the file types, sometimes when the documents are scanned with phone cameras, a few challenging problems like image skewness, rotation, brightness, or low-resolution should also be handled. Thereby, we had to make sure that bots classify these documents into the structured, semi-structured, or unstructured category, thus saving it in a generic format.
After arranging and classifying the documents into a generic format, the next step was to digitalize them using OCR technique. With this, the text can be retrieved together with its location in co-coordinates from the images. This helps to standardize the documents and data for the subsequent steps. A few challenges were encountered when OCR software could not correctly distinguish between characters, such as ‘t’ versus ‘i,’ or ‘0’ versus ‘O’. For this specific problem, Machine Learning was introduced as will be described in the next step.
After the data is digitized, the OCR software should understand the kind of document it’s working with and what’s relevant. But the traditional OCR software can struggle to scale document classification efforts. Hence software bots were trained with cognitive abilities by leveraging machine learning and deep learning techniques to make the OCRs more intelligent. ML-based OCR solutions can identify a document type and match it against a known document type used by your business. They can also parse and understand blocks of text in unstructured documents. Once the solution knows more about the document itself, it can begin to extract relevant information based on intent and meaning.
Data extraction is the core of Document Understanding. As discussed in the previous section on Integrating RPA’s with OCR in this step, opt the data extraction technique based on the type of document. Through RPAs, we can easily configure which extractor to use, whether a rule-based or ML-based or a hybrid model OCR technique. Based on the confidence and performance metrics that are returned after the information extraction, the software robots will save them in our desired format for further analysis.
OCR and Machine Learning models are not a hundred percent accurate in terms of information extraction, hence adding a layer of human intervention with the help of robots can solve the problem. The way this validation works is that whenever the robots deal with low accuracy and exceptions, it immediately raises a notification to the action center where an employee can receive a request to validate data or handle exceptions and can solve any uncertainties in a matter of clicks. Further, we can unlock the potential of Artificial Intelligence to document data over time to make predictions, and identify potential anomalies that may indicate fraud, duplication, and other errors.