Maximizing Text Recognition Accuracy with Image Transformers in Spark OCR

Spark OCR is an object character recognition library that can scale natively on any Spark cluster; enables processing documents privately without uploading them to a cloud service; and most importantly, provides state-of-the-art accuracy for a variety of common use cases. A primary method of maximizing NLP OCR accuracy is using a set of pre-built image pre-processing transformers – for noise reduction, skew correction, object removal, automated scaling, erosion, binarization, and dilation. These transformers can be combined into OCR pipelines that effectively resolve common ‘document noise’ issues that reduce OCR accuracy.

This webinar describes real-world OCR use cases, common accuracy issues they bring, and how to use image transformers in Spark OCR in order to resolve them at scale. Example Python code will be shared using executable notebooks that will be made publicly available.