Development of text extraction technique using optical character recognition and morphological reconstruction to eliminate artifacts of image’s background

Wasan M Jwaid

doi:10.15587/1729-4061.2022.252803

Abstract

Text recognition of images is beneficial in a wide range of computer vision purposes such as robot navigation, document analysis, and image search. The optical character recognition (OCR) technique presents a simple tool to combine text recognition functionality to many industrial and educational applications. Best OCR results can be acquired when the background of the text image is uniform and appears as a document picture. In contrast, the challenges to recognizing accurate texts occur when the image has a non-uniform background that require further preprocessing to obtain acceptable OCR result. This work discusses three scenarios. Initially, this work will test the OCR on a normal business card as an image with a uniform background. Next, discusses the text recognition of a keypad image including digits with a non-uniform background. Here, there are two preprocessing algorithms used to enhance the OCR function to overcome the negative effect of the non-uniform background of images and to detect text with high accuracy. Finally, the developed OCR method is tested on different scanned bills and discusses the variation of the obtained results. The two algorithms are the morphological reconstruction to eliminate artifacts and create cleaner images to be further processed by OCR and the Region of Interest ROI-based OCR to spot explicit regions in a tested image. Verification for the effectiveness of the Morphological-based OCR over the ROI-based method has been conducted on a dataset of scanned electricity bills images with an accuracy of 98.2 % for Morphological-based while it is only about 89.3 % for ROI-based OCR.

Highlights

Optical Character Recognition (OCR) is a method of extracting text from photographs and translating it to an electronic format
The negative effect of the non-uniform background is pre-processed using two algorithms: 1) morphological reconstruction algorithm, which is employed to eliminate artifacts and create cleaner images to be further processed by OCR; 2) ROI-based preprocessing method to spot explicit regions in a tested image, which the OCR must process
These results show that the Morphological reconstruction algorithm with the OCR method performs better than ROIbased preprocessing with OCR as the latter marks characters and things other than the numbers

Summary

Introduction

Optical Character Recognition (OCR) is a method of extracting text from photographs and translating it to an electronic format. The first step is text detection, which determines the textual portion of the image. The second component of OCR, text recognition, is where the text is recovered from the image, and this localization of text inside the image is crucial. Pattern recognition in the second approach identifies the character as a whole. A line of text can be identified by looking for rows of white pixels separated by rows of black pixels. We make a circle with that radius and divide it into smaller portions. At this stage, the algorithm will compare each subpart and will be able to send a database of matrices representing characters in different fonts. It’s simple to move printed media into the digital world if you do this for every line in every character

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