Don’t Miss the Fine Print! An Enhanced Framework to Extract Text from Low Resolution Images

Pranay Dugar; Aditya Vikram; Anirban Chatterjee; Kunal Banerjee; Vijay Agneeswaran

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Paper

Don’t Miss the Fine Print! An Enhanced Framework to Extract Text from Low Resolution Images

Topics: Deep Learning for Visual Understanding ; Image Enhancement and Restoration; Machine Learning Technologies for Vision

In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5 VISAPP: VISAPP, 664-671, 2022

Authors: Pranay Dugar ¹ ; Aditya Vikram ² ; Anirban Chatterjee ¹ ; Kunal Banerjee ¹ and Vijay Agneeswaran ¹

Affiliations: ¹ Walmart Global Tech, Bangalore, India ; ² Flipkart, Bangalore, India

Keyword(s): Scene Text Recognition, Super-resolution, Text Extraction, Convolution Neural Network.

Abstract: Scene Text Recognition (STR) enables processing and understanding of the text in the wild. However, roadblocks like natural degradation, blur, and uneven lighting in the captured images result in poor accuracy during detection and recognition. Previous approaches have introduced Super-Resolution (SR) as a processing step between detection and recognition; however, post enhancement, there is a significant drop in the quality of the reconstructed text in the image. This drop is especially significant in the healthcare domain because any loss in accuracy can be detrimental. This paper will quantitatively show the drop in quality of the text in an image from the existing SR techniques across multiple optimization-based and GAN-based models. We propose a new loss function for training and an improved deep neural network architecture to address these shortcomings and recover text with sharp boundaries in the SR images. We also show that the Peak Signal-to-Noise Ratio (PSNR) and the Structu ral Similarity Index Measure (SSIM) scores are not effective metrics for identifying the quality of the text in an SR image. Extensive experiments show that our model achieves better accuracy and visual improvements against state-of-the-art methods in terms of text recognition accuracy. We plan to add our module on SR in the near future to our already deployed solution for text extraction from product images for our company. (More)

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Paper citation in several formats:

Dugar, P.; Vikram, A.; Chatterjee, A.; Banerjee, K. and Agneeswaran, V. (2022). Don’t Miss the Fine Print! An Enhanced Framework to Extract Text from Low Resolution Images. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP; ISBN 978-989-758-555-5; ISSN 2184-4321, SciTePress, pages 664-671. DOI: 10.5220/0010971100003124

@conference{visapp22,
author={Pranay Dugar. and Aditya Vikram. and Anirban Chatterjee. and Kunal Banerjee. and Vijay Agneeswaran.},
title={Don’t Miss the Fine Print! An Enhanced Framework to Extract Text from Low Resolution Images},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP},
year={2022},
pages={664-671},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010971100003124},
isbn={978-989-758-555-5},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP
TI - Don’t Miss the Fine Print! An Enhanced Framework to Extract Text from Low Resolution Images
SN - 978-989-758-555-5
IS - 2184-4321
AU - Dugar, P.
AU - Vikram, A.
AU - Chatterjee, A.
AU - Banerjee, K.
AU - Agneeswaran, V.
PY - 2022
SP - 664
EP - 671
DO - 10.5220/0010971100003124
PB - SciTePress