An Adaptive Thresholding Algorithm-Based Optical Character Recognition System for Information Extraction in Complex Images

Extracting texts from images with complex backgrounds is a
major challenge today. Many existing Optical Character Recognition
(OCR) systems could not handle this problem. As reported in the literature,
some existing methods that can handle the problem still encounter major
difficulties with extracting texts from images with sharp varying contours,
touching word and skewed words from scanned documents and images
with such complex backgrounds. There is, therefore, a need for new
methods that could easily and efficiently extract texts from these images
with complex backgrounds, which is the primary reason for this work. This
study collected image data and investigated the processes involved in image
processing and the techniques applied for data segmentation. It employed
an adaptive thresholding algorithm to the selected images to properly
segment text characters from the image’s complex background. It then used
Tesseract, a machine learning product, to extract the text from the image
file. The images used were coloured images sourced from the internet with
different formats like jpg, png, webp and different resolutions. A custom
adaptive algorithm was applied to the images to unify their complex
backgrounds. This algorithm leveraged on the Gaussian thresholding
algorithm. The algorithm differs from the conventional Gaussian algorithm
as it dynamically generated the blocksize to apply threshing to the image.
This ensured that, unlike conventional image segmentation, images were
processed area-wise (in pixels) as specified by the algorithm at each
instance. The system was implemented using Python 3.6 programming
language. Experimentation involved fifty different images with complex
backgrounds. The results showed that the system was able to extract
English character-based texts from images with complex backgrounds with
69.7% word-level accuracy and 81.9% character-level accuracy. The
proposed method in this study proved to be more efficient as it
outperformed the existing methods in terms of the character level
percentage accuracy