Image preprocessing and modified adaptive thresholding for improving OCR

TL;DR

This paper introduces a novel image preprocessing technique that enhances OCR accuracy by isolating text features and applying adaptive thresholding based on pixel intensity, validated through experiments with PyTesseract.

Contribution

The proposed method uniquely combines feature removal and adaptive thresholding based on pixel intensity to improve OCR performance.

Findings

Enhanced OCR accuracy with the proposed preprocessing method.

Effective isolation of text features improves thresholding results.

Validated improvements using PyTesseract on processed images.

Abstract

In this paper I have proposed a method to find the major pixel intensity inside the text and thresholding an image accordingly to make it easier to be used for optical character recognition (OCR) models. In our method, instead of editing whole image, I are removing all other features except the text boundaries and the color filling them. In this approach, the grayscale intensity of the letters from the input image are used as one of thresholding parameters. The performance of the developed model is finally validated with input images, with and without image processing followed by OCR by PyTesseract. Based on the results obtained, it can be observed that this algorithm can be efficiently applied in the field of image processing for OCR.