Business documents come in a variety of formats, layouts and degrees of quality. This can include anything from PDFs, Microsoft Word documents, images and more. These files can contain handwriting, be low resolution, have skewed layouts and missing information which makes identifying and extracting key information a tedious and manual process. This manual process leads to reduced staff morale, slow processing times, increased processing costs and poor customer experiences.
The information needed to efficiently and accurately process the myriad of documents that exist today is not fully contained within those documents. Additional context and comprehension are required, which is part of the reason that enterprises have continued to rely on manual data entry operations for so long.
Insurance, financial services, healthcare, logistics and governments are all dealing with high-value transactions, and faced with the trade-off between accuracy and automation. Humans have traditionally been used to manually process all these documents thanks to their domain-specific knowledge and understanding of the context. However, in addition to being time consuming, the tedious work creates many opportunities for attention to lapse and mistakes to be made. Advances in Optical Character Recognition (OCR), Machine Learning and Natural Language are able to help bridge the gap between human understanding and processing, transforming business operations in the process.
Whereas older technologies rely on explicit rules or old OCR relied on perfectly formatted documents, Machine Learning can bridge this gap since it trains on real-world data and continues to learn and adjust itself in response to the data it’s exposed to. Machine Learning has unlocked capabilities that weren not possible before, taking us from a place where we couldn’t possibly write software that accurately reflected the world – including the diverse document types and text inputs it contains – to one that can train and teach itself. When it comes to processing the vast volume of documents generated by and moving between businesses and customers, Machine Learning is the perfect candidate.
By allowing customers to train their model for any document type, Unmand is able to read handwritten and machine-generated text. In addition, by their nature, Machine Learning solutions get better over time, incorporating a feedback loop driven by expert humans to review and resolve the edge cases, which improves the underlying models. By leveraging the latest technologies, organisations can streamline complex document processing workflows and extract valuable information that can be fed into downstream systems to drive better business outcomes.