Enhancing OCR Accuracy with Super Resolution
Ankit Lat, C. V. Jawahar · 2018
Accuracy of OCR is often marred by the poor quality of the input document images. Generally this performance degradation is attributed to the resolution and quality of scanning. This calls for special efforts to improve the quality of document images before passing it to the OCR engine. One compelling option is to super-resolve these low resolution document images before passing them to the OCR engine. In this work we address this problem by super-resolving document images using Generative Adversarial Network (GAN). We propose a super resolution based preprocessing step that can enhance the accuracies of the OCRs (including the commercial ones). Our method is specially suited for printed document images. We validate the utility in wide variety of document images (where fonts, styles, and languages vary) without any pre-processing step to adapt across situations. Our experiments show an improvement upto 21% in accuracy OCR on test images scanned at low resolution. One immediate application of this can be in enhancing the recognition of historic documents which have been scanned at low resolutions.