AI-based document preprocessing for optical character recognition

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Even though automated optical character recognition (OCR) is now a mature technology, it is facing a new challenge these days: sources of documents are now coming from pictures taken by multiple sources including mobile cameras. These images contain significant variations in lighting, distortion, and other artifacts that lowers the accuracy of OCR algorithms. In this research, model OCR as an unknown-box and improve image quality by deep learning-based algorithms suitable for the unknown-box. The unknown-box assumption accurately reflects a reality where end-users do not have access to the components of the OCR, e.g., proprietary software. Our proposed method to improve image quality relies on state-of-the-art advancements in image analysis and computer vision algorithms. The industry partner for the research is Inuit, Inc. and Intuit, Canada who are one of the leaders in automated document processing.