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Pre-Ocr Image Optimization by Reinforcement Learning
(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Gökmen, Muhittin; Gökmen, Muhittin; 02.02. Department of Computer Engineering; 02. Faculty of Engineering; 01. MEF University
Optical Character Recognition technology usage in digital transformation of documents is steadily growing by the help of new hardware and software technologies. However digital image optimization for more accurate OCR results continues to be a problem. In this study, we propose a reinforcement learning based model that learns optimal set of actions to increase OCR accuracy in computer screenshot images. Model input images are identified by their grayscale histogram distributions. An unprocessed base image having 100% OCR accuracy is taken initially. The correlation between the grayscale histograms of base image and input image is used for comparison. We implemented reinforcement learning’s random (or optimal) action and reward approach for creating a Q-table. For measuring image to text conversion success, Tesseract OCR software is used. The introduced approach can improve OCR accuracy especially in bulk image to document conversion jobs. By using optimal actions for single image or bulk images, it can also decrease computational load and time-consumption in image processing.

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