Version 1
: Received: 16 March 2023 / Approved: 17 March 2023 / Online: 17 March 2023 (13:25:06 CET)
How to cite:
shafi, S.; Atif, A.; Shafi, H. Automated Document Orientation Correction Using Skew and Inversion Rectification With Hough Line Transformation and Machine Learning. Preprints2023, 2023030326. https://doi.org/10.20944/preprints202303.0326.v1
shafi, S.; Atif, A.; Shafi, H. Automated Document Orientation Correction Using Skew and Inversion Rectification With Hough Line Transformation and Machine Learning. Preprints 2023, 2023030326. https://doi.org/10.20944/preprints202303.0326.v1
shafi, S.; Atif, A.; Shafi, H. Automated Document Orientation Correction Using Skew and Inversion Rectification With Hough Line Transformation and Machine Learning. Preprints2023, 2023030326. https://doi.org/10.20944/preprints202303.0326.v1
APA Style
shafi, S., Atif, A., & Shafi, H. (2023). Automated Document Orientation Correction Using Skew and Inversion Rectification With Hough Line Transformation and Machine Learning. Preprints. https://doi.org/10.20944/preprints202303.0326.v1
Chicago/Turabian Style
shafi, S., Amara Atif and Huzaib Shafi. 2023 "Automated Document Orientation Correction Using Skew and Inversion Rectification With Hough Line Transformation and Machine Learning" Preprints. https://doi.org/10.20944/preprints202303.0326.v1
Abstract
Document deskewing is a fundamental problem in document image processing. While existing methods have limitations, such as Hough Line Transformation that can deskew images upside down, and Deep Learning models that require huge amounts of human labour and computational resources and still fail to deskew while taking care of orientation, OCR-based methods also struggle to read text when it is tilted. In this paper, we propose a novel, simple, cost-effective deep learning method for fixing the skew and orientation of documents. Our approach reduces the search space for the machine learning model to predict whether an image is upside down or not, avoiding the huge search space of predicting an angle between 0 and 360. We finetuned a MobileNetV2 model, which was pre-trained on imagenet, using only 200 images and achieve good results. This method is useful for automation-based tasks, such as data extraction using OCR technology, and can greatly reduce manual labour.
Keywords
document image processing; deskew; Hough Line Transform; image rectification; machine learning; OCR; document orientation; image preprocessing; computer vision; AI
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
