Histogram Equalization Implementation in the Preprocessing Phase on Optical Character Recognition

A 2014 report from Digital Marketing Philippines stated that the number of web applications with visual content as their main product has increased significantly. Image processing technology has also undergone significant growth. One example of this is optical character recognition (OCR), which can convert the text on an image to plain text. However, a problem occurs when the image has low contrast and low exposure, which potentially results in information being hidden in the image. To address this problem, histogram equalization is used to enhance the image’s contrast so the hidden information can be shown. Similar to X-ray scanning used in the medical field, histogram equalization processes scanned images that have low brightness and low contrast. In this study, histogram equalization was successfully implemented using OCR preprocessing. The test was done with a dataset that contains dark background images with low light text; the successful outcome resulted in the ability to show 74.95% of the information hidden in the image.

A 2014 report from Digital Marketing Philippines stated that the number of web applications with visual content as their main product has increased significantly. Image processing technology has also undergone significant growth. One example of this is optical character recognition (OCR), which can convert the text on an image to plain text. However, a problem occurs when the image has low contrast and low exposure, which potentially results in information being hidden in the image. To address this problem, histogram equalization is used to enhance the image’s contrast so the hidden information can be shown. Similar to X-ray scanning used in the medical field, histogram equalization processes scanned images that have low brightness and low contrast. In this study, histogram equalization was successfully implemented using OCR preprocessing. The test was done with a dataset that contains dark background images with low light text; the successful outcome resulted in the ability to show 74.95% of the information hidden in the image.