In today’s rapidly evolving era, most industries face the challenge of handling an extensive range of documents as part of their existing complex workflows. Whether it’s policy management, handling claims, legal documents, financial documents and so on - each industry has to manage and process an enormous volume of information.
Consider the Insurance industry as an example - a typical workflow in the insurance industry involves
- Users submitting completed documents to the Insurer
- Humans constantly look at the common Inbox which is reserved for incoming documents and route them to the correct step
- Finally, an expert looks at the contents of the email, attachments and the context of the email to form a correct response.
As the volume, variance and complexity of documents grow, these traditional document processing methods can become time-consuming, error-prone and labor-intensive resulting in significant delays, increased costs and reduced stickiness of customers.
By leveraging the power of ML, Computer Vision (CV) technologies automatically sort through various document types, recognize the presence of text even in highly dense areas and extract relevant information. Over the past decade, these technologies have evolved to be able to extract content even from extremely challenging formats like scanned, handwritten and smudged inputs.
Despite the many benefits that CV offers, there are several challenges to overcome -
- Dealing with low quality input data - blurred images, areas of high and low contrast, low quality scans, smudged documents and more
- Recognizing Human handwriting
- Understanding complex structural elements like Tables, Checkmarks, Signatures, etc
- Identifying structural relationships between various pieces of information to accurately extract key: value pairs
Over the past few years, Ushur has developed an entire suite of ML models and Computer Vision algorithms to deal with such situations. Our Intelligent Document Automation (IDA) stack utilizes advanced tools and techniques that include Computer Vision, Image Recognition and Data Extraction techniques to effectively tackle such complex document processing challenges.
Some of the Computer Vision techniques include:
- Optical Character Recognition (OCR):
This is the foundational block of Document Processing - OCR is a technique that enables Machines to identify and read text from images. Imagine taking a picture of the document with your phone and instantly converting it into an editable text document! OCR makes it possible by recognising the letters and words in the image and turning them into digital text that you can edit, search and analyze.
- Image Classification:
Image Classification techniques facilitate categorisation of documents by analyzing the layout and content. For example, by quickly identifying a Driver’s License or Passport, businesses can instantly verify a person’s age.
- Object Detection and Recognition:
These models identify and capture various objects located in an image - think of text, tables, checkmarks and so on. Particularly in densely populated documents, correct detection is crucial to ensure key information is not missed. For example, detecting the marked checkbox can quickly identify the type of policy, while table detection can extract intricate details, such as coverage amounts and deductibles. Image features like edges, color, contrast etc are used to identify a particular object.
- Multi Modal Language Models
Extracting relevant information from documents is the cornerstone of document processing. One of the most common techniques used to extract information from text is Named Entity Recognition (NER). This technique is combined with image layout models to understand the text and layout interactions. So, a combination of these methods are used -
- Textual
- Layout
- Structural
- Semantic
Our IDA stack is powerful, flexible and has all the necessary ingredients in place to solve several business problems across industries:
- Faster Claims Processing: IDA streamlines claims processing by automating data entry and reducing manual effort.
- Improved Customer Experience: IDA helps insurers provide faster and more personalized services to customers.
- Improved Data Accuracy: IDA reduces errors in data entry and extraction, which improves the accuracy of information used in claims processing, underwriting, and risk management. This helps insurers make better decisions and reduce risk.
- Cost Savings: IDA reduces the need for manual labor, which can lead to significant cost savings for insurers.