Optical character recognition (OCR) extracts printed or handwritten text from images, scanned documents, or other visual sources and converts it into machine-readable digital text. So why doesn’t every company use OCR to automate ESG reporting? It’s not that simple. Getting it to work properly involves complex, tedious coding and technical work that requires specialized skills and time to implement. If you lack either the experience or the time to do this yourself, RapidCloud has you covered.
The OCR we selected for ESG reporting energy invoice automation for RapidCloud had to meet two critical criteria. First, it must perform with a high degree of accuracy across a wide array of document types, formats, and qualities. Second, it needs the capability to constantly learn. We have found Amazon Textract to be a strong match for both criteria and have integrated it into RapidCloud’s ESG reporting automation.
Amazon Textract has a machine learning-based approach that requires exposure to a diverse set of data to accurately recognize and interpret text within images. The OCR in RapidCloud has been trained to interpret variations in appearance (font, size, orientation, etc.), eliminate noise and distortion that are not relevant, learn from mistakes and iteratively improve accuracy, perform at the lowest possible computational costs, and much more. Amazon Textract distinguishes each document type, and understands, identifies, extracts, and organizes the precise data you need to report on in the format you require.
Amazon Textract also provides you with the level of confidence that it has read and applied the data correctly. For example, reading a clear PDF utility invoice, it may have 99% confidence it is correct. But with a wrinkled, sloppily handwritten receipt that is scanned diagonally, it may only be 40% confident. When Amazon Textract has a low level of confidence, RapidCloud will direct the document to a human for verification or correction. The human loop is critical for maintaining data quality in your ESG reporting energy invoice automation.
Applying rules is another critical step to improve the accuracy and reliability of the extracted energy invoices. In RapidCloud we have implemented preprocessing rules for image enhancement, noise reduction, and deskewing to provide cleaner input to the OCR. When it comes to the text we apply things like ESG reporting contextual rules to ensure consistency in terminology, pattern matching to detect common numbers, formats, products, etc., feedback loops so Amazon Textract learns from mistakes, and much more. This is key to ensuring you have the right, accurate data for your ESG reporting. RapidCloud has a rich set of rules already built, many of which will apply to your energy invoices out of the box.
In the end, it’s just a matter of integration, so your clean, accurate, timely data is automatically routed into your ESG reporting system.
