survey

Document Image Quality Assessment: A Survey

Authors:

David Doermann,

Umapada PalAuthors Info & Claims

ACM Computing Surveys, Volume 56, Issue 2

Article No.: 29, Pages 1 - 36

https://doi.org/10.1145/3606692

Published: 14 September 2023 Publication History

Abstract

The rapid emergence of new portable capturing technologies has significantly increased the number and diversity of document images acquired for business and personal applications. The performance of document image processing systems and applications depends directly on the quality of the document images captured. Therefore, estimating the document's image quality is an essential step in the early stages of the document analysis pipeline. This article surveys research on Document Image Quality Assessment (DIQA). We first provide a detailed analysis of both subjective and objective DIQA methods. Subjective methods, including ratings and pair-wise comparison-based approaches, are based on human opinions. Objective methods are based on quantitative measurements, including document modeling and human perception-based methods. Second, we summarize the types and sources of document degradations and techniques used to model degradations. In addition, we thoroughly review two standard measures to characterize document image quality: Optical Character Recognition (OCR)-based and objective human perception-based. Finally, we outline open challenges regarding developing DIQA methods and provide insightful discussion and future research directions for this problem. This survey will become an essential resource for the document analysis research community and serve as a basis for future research.

References

[1]

D. Doermann, E. Rivlin, and A. Rosenfeld. 1998. The function of documents. Image Vis. Comput. 16, 11 (1998), 799–814. DOI:

[2]

International Standards - ISO/TC 42. 2022. Retrieved September 23, 2022 from https://www.imaging.org/site/IST/Standards/TC_42__Standards_/IST/Standards/TC42.aspx?hkey=208a8a91-14da-4b6a-a92d-7637238308e4.

[3]

ISO 20462-1:2005 Photography — Psychophysical experimental methods for estimating image quality — Part 1: Overview of psychophysical elements. 2022. Retrieved September 23, 2022 from https://www.iso.org/standard/38330.html.

[4]

A. Mittal, A. K. Moorthy, A. C. Bovik, and L. K. Cormack. 2011. Automatic prediction of saliency on JPEG distorted images. In Proceedings of the International Workshop on Quality of Multimedia Experience. 195–200.

[5]

V. Govindaraju and S. N. Srihari. 1995. Image quality and readability. In Proceedings of the International Conference on Image Processing. 324–327. DOI:

[6]

A. Alaei, R. Raveaux, D. Conte, and B. Stantic. 2018. ‘Quality’ vs. “Readability” in document images: Statistical analysis of human perception. In Proceedings of the International Workshop on Document Analysis Systems. 363–368.

[7]

L. Hu, Z. Hu, P. Bauer, T. Harris, P. Allebach. 2020. Document image quality assessment with relaying reference to determine minimum readable resolution for compression. In Proceedings of the IS&T Int.l. Symp. on Electronic Imaging: Image Quality and System Performance XVII, 2020, 323-1--323-8.

[8]

A. Radford, K. Narasimhan, T. Salimans, and I. Sutskever. 2018. Improving language understanding by generative pre-training. https://www.mikecaptain.com/resources/pdf/GPT-1.pdf.

[9]

J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the NAACL-HLT 2019, 4171--4186.

[10]

A. Alaei, D. Conte, M. Martineau, and R. Raveaux. 2018. Blind document image quality prediction based on a modification of quality-aware clustering method integrating a patch selection strategy. Expert Systems with Applications 108 (2018), 183–192. DOI:

Digital Library

[11]

A. Shahkolaei, H. Z. Nafchi, S. Al-Maadeed, and M. Cheriet. 2018. Subjective and objective quality assessment of degraded document images. Journal of Cultural Heritage 30 (2018), 199–209. DOI:

[12]

A. Shahkolaei, A. Beghdadi, and M. Cheriet. 2019. Blind quality assessment metric and degradation classification for degraded document images. Signal Process. Signal Processing: Image Communication 76 (2019), 11–21.

Digital Library

[13]

R. T. Hartley and K. Crumpton. 1999. Quality of OCR for degraded text images. In Proceedings of the ACM Conference on Digital libraries. 228–229. DOI:

Digital Library

[14]

P. Ye and D. Doermann. 2013. Document image quality assessment: A brief survey. In Proceedings of the International Conference On Document Analysis and Recognition. 723–727.

Digital Library

[15]

E. N. Dalal, Allan Haley, Mark Robb, Dale Mashtare, John Briggs, Paul L. Jeran, Ted Bouk, and John Deubert. 2003. INCITS W1. 1 standards for perceptual evaluation of text and line quality. In Proceedings of the Image Quality and System Performance, International Society for Optics and Photonics. 34–38.

[16]

S. Athar and Z. Wang. 2019. A comprehensive performance evaluation of image quality assessment algorithms. IEEE Access 7 (2019), 140030–140070. DOI:

[17]

T. Schlett, C. Rathgeb, O. Henniger, J. Galbally, J. Fierrez, and C. Busch. 2021. Face image quality assessment: A literature survey. ACM Computing Surveys. DOI:

Digital Library

[18]

R. D. Lins. 2009. A taxonomy for noise in images of paper documents-the physical noises. In Proceedings of the International Conference Image Analysis and Recognition. Springer, 844–854.

Digital Library

[19]

T. Lu and A. Dooms. 2018. Towards physical distortion identification and removal in document images. In Proceedings of the European Workshop on Visual Information Processing. 1–6. DOI:

[20]

Nikolay Ponomarenko, Vladimir Lukin, Alexander Zelensky, Karen Egiazarian, Jaakko Astola, Marco Carli, and Federica Battisti. 2009. An OCR assessment of the quality of document images acquired with portable digital cameras. Presented at the Camera based Document Analysis and Recognition, Barcelona, Spain, 1–5.

[21]

Rafael Dueire Lins, G. P. e Silva, and A. R. G. e Silva. 2007. Assessing and improving the quality of document images acquired with portable digital cameras. In Proceedings of the International Conference on Document Analysis and Recognition. 569–573.

[22]

C. L. Tan, L. Zhang, Z. Zhang, and T. Xia. 2006. Restoring warped document images through 3D shape modeling. IEEE Transactions on Pattern Analysis and Machine Intelligence 28, 2 (2006), 195–208. DOI:

Digital Library

[23]

E. B. Smith and X. Qiu. 2003. Statistical image differences, degradation features, and character distance metrics. Document Analysis and Recognition 6 (2003), 3.

[24]

Z. Wang, A. C. Bovik, and E. P. Simoncelli. 2005. Structural approaches to image quality assessment. Handb. Image Video Process 7, 18 (2005), 2--33.

[25]

Zhou Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli. 2004. Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing 13, 4 (2004), 600–612. DOI:

Digital Library

[26]

A. Alaei, D. Conte, and R. Raveaux. 2015. Document image quality assessment based on improved gradient magnitude similarity deviation. In Proceedings of the International Conference on Document Analysis and Recognition. 176–180.

Digital Library

[27]

Z. Wang and A. C. Bovik. 2009. Mean squared error: Love it or leave it? A new look at signal fidelity measures. IEEE Signal Processing Magazine 26, 1 (2009), 98–117. DOI:

[28]

T. Lu and A. Dooms. 2019. A deep transfer learning approach to document image quality assessment. In Proceedings of the 2019 International Conference on Document Analysis and Recognition. 1372–1377.

[29]

H. Yang, Y. Fang, and W. Lin. 2015. Perceptual Quality Assessment of Screen Content Images. Transactions on Image Processing 24, 11 (2015), 4408–4421. DOI:

Digital Library

[30]

P. Singh, E. Vats, and A. Hast. 2018. Learning surrogate models of document image quality metrics for automated document image processing. In Proceedings of the International Workshop on Document Analysis Systems. 67–72. DOI:

[31]

J. Courtney. 2021. SEDIQA: Sound emitting document image quality assessment in a reading aid for the visually impaired. Journal of Imaging 7, 9 (2021), 168. DOI:

[32]

J. Courtney. 2020. CleanPage: Fast and clean document and whiteboard capture. Journal of Imaging 6 (2020), 10. DOI:

[33]

F. Drira, F. LeBourgeois, and H. Emptoz. 2012. A new PDE-based approach for singularity-preserving regularization: Application to degraded characters restoration. International Journal on Document Analysis and Recognition 15, 3 (2012), 183–212. DOI:

Digital Library

[34]

N. Nayef, J. Chazalon, P. Gomez-Krämer, and J.-M. Ogier. 2014. Efficient example-based super-resolution of single text images based on selective patch processing. In Proceedings of the International Workshop on Document Analysis Systems. 227–231. DOI:

Digital Library

[35]

H. S. Baird. 2007. The state of the art of document image degradation modelling. In Proceedings of the Digital Document Processing. Advances in Pattern Recognition, Springer. 261–279.

[36]

C. Yáñez and J. Tapia. 2021. Image quality assessment on identity documents. In Proceedings of the International Conference of the Biometrics Special Interest Group. 1–5. DOI:

[37]

C. Hale and E. Barney-Smith. 2007. Human image preference and document degradation models. In Proceedings of the International Conference on Document Analysis and Recognition. 257–261.

[38]

J. Kumar, F. Chen, and D. Doermann. 2012. Sharpness estimation for document and scene images. In Proceedings of the International Conference on Pattern Recognition. 3292–3295.

[39]

T. Obafemi-Ajayi and G. Agam. 2011. Character-based automated human perception quality assessment in document images. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans 42, 3 (2011), 584--595.

[40]

A. Shahkolaei, A. Beghdadi, S. Al-Máadeed, and M. Cheriet. 2018. MHDID: A multi-distortion historical document image database. In Proceedings of the International Workshop on Arabic and Derived Script Analysis and Recognition. 156–160.

[41]

X. Peng, H. Cao, K. Subramanian, R. Prasad, and P. Natarajan. 2011. Automated image quality assessment for camera-captured OCR. In Proceedings of the International Conference on Image Processing. 2621–2624.

[42]

A. Souza, M. Cheriet, S. Naoi, and C. Y. Suen. 2003. Automatic filter selection using image quality assessment. In Proceedings of the International Conference on Document Analysis and Recognition. 508–512.

[43]

P. Ye and D. Doermann. 2012. Learning features for predicting OCR accuracy. In Proceedings of the International Conference on Pattern Recognition. 3204–3207.

[44]

N. Nayef and J.-M. Ogier. 2015. Metric-based no-reference quality assessment of heterogeneous document images. In Proceedings of the Document Recognition and Retrieval XXII, International Society for Optics and Photonics. 94020L.

[45]

L. R. Blando, J. Kanai, and T. A. Nartker. 1995. Prediction of OCR accuracy using simple image features. In Proceedings of the International Conference On Document Analysis and Recognition. 319–322.

[46]

D. Kumar and A. Ramakrishnan. 2011. QUAD: Quality assessment of documents. In Proceedings of the International Workshop on Camera based Document Analysis and Recognition. 79–84.

[47]

M. Cannon, J. Hochberg, and P. Kelly. 1999. Quality assessment and restoration of typewritten document images. International Journal on Document Analysis and Recognition 2 (1999), 80–89.

[48]

S.-L. Chou and S.-S. Yu. 1993. Sorting qualities of handwritten Chinese characters for setting up a research database. In Proceedings of the International Conference on Document Analysis and Recognition. 474–477.

[49]

V. Kulesh, K. Schaffer, I. Sethi, and M. Schwartz. 2001. Handwriting quality evaluation. In Proceedings of the International Conference on Advances in Pattern Recognition. Springer. 157–165.

Digital Library

[50]

J.-S. Park, H.-J. Kang, and S.-W. Lee. 2000. Automatic quality measurement of gray-scale handwriting based on extended average entropy. In Proceedings of the International Conference on Pattern Recognition. 426–429.

[51]

A. Alaei, D. Conte, M. Blumenstein, and R. Raveaux. 2016. Document image quality assessment based on texture similarity index. In Proceedings of the International Workshop on Document Analysis Systems. 132–137.

[52]

A. Alaei. 2019. A new document image quality assessment method based on Hast derivations. In Proceedings of the International Conference on Document Analysis and Recognition. 1244–1249. DOI:

[53]

K. Gu, G. Zhai, W. Lin, X. Yang, and W. Zhang. 2016. Learning a blind quality evaluation engine of screen content images. Neurocomputing 196 (2016), 140–149.

Digital Library

[54]

H. Li, J. Qiu, and F. Zhu. 2018. TextNet for text-related image quality assessment. In Proceedings of the International Conference on Artificial Neural Networks. Springer. 275–285.

[55]

R. Biedert, A. Dengel, M. Elshamy, and G. Buscher. 2012. Towards robust gaze-based objective quality measures for text. In Proceedings of the Symposium on Eye Tracking Research and Applications. 201–204.

Digital Library

[56]

M. Cheriet and R. F. Moghaddam. 2008. DIAR: Advances in degradation modeling and processing. In Proceedings of the International Conference Image Analysis and Recognition. Springer, 1–10.

Digital Library

[57]

H. S. Baird. 1992. Document image defect models. In Proceedings of the Structured Document Image Analysis. Springer. 546–556.

[58]

R. Garg and S. Chaudhury. 2016. Automatic selection of parameters for document image enhancement using image quality assessment. In Proceedings of the International Workshop on Document Analysis Systems. 422–427.

[59]

T. K. Ho and H. S. Baird. 1997. Large-scale simulation studies in image pattern recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 19, 10 (1997), 1067–1079.

Digital Library

[60]

H. S. Baird. 1993. Calibration of document image defect models. In Proceedings of the Annual Symposium on Document Analysis and Information Retrieval. 1–16.

[61]

H. S. Baird. 1999. Document image quality: Making fine discriminations. In Proceedings of the International Conference on Document Analysis and Recognition. 459–462.

[62]

H. S. Baird. 1991. A 100-font classifier. In Proceedings of the International Conference On Document Analysis and Recognition. St.-Malo, France, 1–13.

[63]

H. S. Baird. 1993. Document image defect models and their uses. In Proceedings of the International Conference on Document Analysis and Recognition. 62–67.

[64]

T. Kanungo, R. M. Haralick, and I. Phillips. 1993. Global and local document degradation models. In Proceedings of the International Conference on Document Analysis and Recognition. 730–734.

[65]

T. Kanungo. 1996. Document Degradation Models and a Methodology for Degradation Parameter Validation. Ph.D. Thesis, University of Washington, Seatle.

Digital Library

[66]

Q. Zheng and T. Kanungo. 2001. Morphological degradation models and their use in document image restoration. In Proceedings of the International Conference on Image Processing. 193–196.

[67]

T. Kanungo and Q. Zheng. 2004. Estimating degradation model parameters using neighborhood pattern distributions: An optimization approach. IEEE Transactions on Pattern Analysis and Machine Intelligence 26, 4 (2004), 520–524.

Digital Library

[68]

S. Sural and P. Das. 1999. A two-state Markov chain model of degraded document images. In Proceedings of the International Conference on Document Analysis and Recognition. 463–466.

[69]

R. F. Moghaddam and M. Cheriet. 2009. Low quality document image modeling and enhancement. International Journal of Document Analysis and Recognition 11 (2009), 4.

Digital Library

[70]

G. Zi. 2005. GroundTruth Generation and Document Image Degradation. Master thesis, University of Maryland, College Park. [Online]. Available: https://drum.lib.umd.edu/handle/1903/2524.

[71]

G. Zi and D. Doermann. 2005. GroundTruth Generation and Document Image Degradation. Maryland University, College Park, Department of Computer Science.

[72]

J. Pan, Z. Hu, Z. Su, and M.-H. Yang. 2017. L0-Regularized intensity and gradient prior for deblurring text images and beyond. IEEE Transactions on Pattern Analysis and Machine Intelligence 39, 2 (2017), 342–355. DOI:

Digital Library

[73]

D. Capel and A. Zisserman. 2000. Super-resolution enhancement of text image sequences. In Proceedings of the International Conference on Pattern Recognition. 600–605.

[74]

E. H. B. Smith. 1998. Characterization of image degradation caused by scanning. Pattern Recognition Letters 19, 13 (1998), 1191--1197.

[75]

M. Norris and E. H. Barney Smith. 2004. Printer modeling for document imaging. In Proceedings of the International Conference on Imaging Science, Systems, and Technology. Las Vegas, NV, 14–20.

[76]

E. H. B. Smith. 2001. Scanner parameter estimation using bilevel scans of star charts. In Proceedings of the International Conference On Document Analysis and Recognition. 1164–1168.

[77]

X. Peng, H. Cao, and P. Natarajan. 2015. Document image OCR accuracy prediction via latent Dirichlet allocation. In Proceedings of the International Conference on Document Analysis and Recognition. 771–775.

Digital Library

[78]

G. Agam, G. Bal, G. Frieder, and O. Frieder. 2007. Degraded document image enhancement. In Proceedings of the Document Recognition and Retrieval XIV, International Society for Optics and Photonics. 65000C.

[79]

Z. Shi and V. Govindaraju. 2004. Historical document image enhancement using background light intensity normalization. In Proceedings of the International Conference on Pattern Recognition. 473–476.

[80]

E. Kavallieratou and E. Stamatatos. 2006. Improving the quality of degraded document images. In Proceedings of the International Conference on Document Image Analysis for Libraries. 10.

Digital Library

[81]

Nikolaos Ntogas and Dimitrios Ventzas. 2009. Binarization of pre-filtered historical manuscripts images. International Journal of Intelligent Computing and Cybernetics 2, 1 (2009), 148–174. DOI:

[82]

W. A. Mustafa, S. Sam, M. A. Jamlos, and W. Khairunizam. 2021. Effect of different filtering techniques on medical and document image. In Proceedings of the National Technical Seminar on Unmanned System Technology. Z. Md Zain, H. Ahmad, D. Pebrianti, M. Mustafa, N. R. H. Abdullah, R. Samad, and M. Mat Noh (Eds.). Singapore: Springer Singapore. 727–736.

[83]

D. K. Reed and E. H. B. Smith. 2008. Correlating degradation models and image quality metrics. In Proceedings of the Document Recognition and Retrieval XV, International Society for Optics and Photonics. 681508.

[84]

T. Kanungo, H. S. Baird, and R. M. Haralick. 1995. Validation and estimation of document degradation models. Presented at the Symposium for Document Analysis and Information Retrieval. Las Vegas, NV, 217–228.

[85]

T. Kanungo, R. M. Haralick, and H. S. Baird. 1995. Power functions and their use in selecting distance functions for document degradation model validation. In Proceedings of the International Conference On Document Analysis and Recognition. 734–739.

[86]

T. Kanungo, R. M. Haralick, H. S. Baird, W. Stuezle, and D. Madigan. 2000. A statistical, nonparametric methodology for document degradation model validation. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 11 (2000), 1209--1223.

[87]

Y. Li, D. Lopresti, G. Nagy, and A. Tomkins. 1996. Validation of image defect models for optical character recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 18, 2 (1996), 99–107.

Digital Library

[88]

R. Walha, F. Drira, F. Lebourgeois, C. Garcia, and A. M. Alimi. 2018. Handling noise in textual image resolution enhancement using online and offline learned dictionaries. International Journal on Document Analysis and Recognition 21 (2018), 1–2.

Digital Library

[89]

P. Sharma and S. Sharma. 2016. An analysis of vision based techniques for quality assessment and enhancement of camera-captured document images. In Proceedings of the International Conference-Cloud System and Big Data Engineering. 425–428.

[90]

H. Lu, A. C. Kot, and Y. Q. Shi. 2004. Distance-reciprocal distortion measure for binary document images. IEEE Signal Processing Letters 11, 2 (2004), 228--231.

[91]

H. Li, F. Zhu, and J. Qiu. 2018. CG-DIQA: No-reference document image quality assessment based on character gradient. In Proceedings of the International Conference on Pattern Recognition. 3622–3626.

[92]

X. Peng, H. Cao, and P. Natarajan. 2016. Document image quality assessment using discriminative sparse representation. In Proceedings of the International Workshop on Document Analysis Systems. 227–232.

[93]

L. Kang, P. Ye, Y. Li, and D. Doermann. 2014. A deep learning approach to document image quality assessment. In Proceedings of the 2014 IEEE International Conference on Image Processing. 2570–2574.

[94]

M. Rusiñol, J. Chazalon, and J.-M. Ogier. 2014. Combining focus measure operators to predict ocr accuracy in mobile-captured document images. In Proceedings of the International Workshop on Document Analysis Systems. 181–185.

Digital Library

[95]

T. K. Bhowmik, T. Paquet, and N. Ragot. 2014. OCR performance prediction using a bag of allographs and support vector regression. In Proceedings of the International Workshop on Document Analysis Systems. 202–206.

Digital Library

[96]

J. Kumar, P. Ye, and D. Doermann. 2013. A dataset for quality assessment of camera-captured document images. In Proceedings of the International Workshop on Camera-Based Document Analysis and Recognition. Springer, 113–125.

[97]

X. Zhu and P. Milanfar. 2010. Automatic parameter selection for denoising algorithms using a no-reference measure of image content. IEEE Transactions on Image Processing 19, 12 (2010), 3116–3132. DOI:

Digital Library

[98]

J. Shemiakina, E. Limonova, N. Skoryukina, V. V. Arlazarov, and D. P. Nikolaev. 2021. A method of image quality assessment for text recognition on camera-captured and projectively distorted documents. Mathematics 9, 17 (2021), 1–22. DOI:

[99]

X. Peng and C. Wang. 2020. Camera captured DIQA with linearity and monotonicity constraints. In Proceedings of the International Workshop on Document Analysis Systems. X. Bai, D. Karatzas, and D. Lopresti (Eds.). Springer International Publishing. 168–181.

Digital Library

[100]

J. Xu, P. Ye, Q. Li, Y. Liu, and D. Doermann. 2016. No-reference document image quality assessment based on high order image statistics. In Proceedings of the International Conference on Image Processing. 3289–3293.

[101]

H. Li, F. Zhu, and J. Qiu. 2019. Towards document image quality assessment: A text line based framework and a synthetic text line image dataset. In Proceedings of the International Conference on Document Analysis and Recognition. 551–558. DOI:

[102]

P. Li, L. Peng, J. Cai, X. Ding, and S. Ge. 2017. Attention based RNN model for document image quality assessment. In Proceedings of the International Conference on Document Analysis and Recognition. 819–825.

[103]

R. Shinghal and C. Y. Suen. 1982. A method for selecting constrained hand-printed character shapes for machine recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 4, 1 (1982), 74–78. DOI:. 21869007.

Digital Library

[104]

H. Hase, M. Yoneda, M. Sakai, and J. Yoshida. 1988. Evaluation of handprinting variation of characters using variation entropy. IEICE Transactions 71, 6 (1988), 1048–1056.

[105]

H. Hase. 2011. Quality evaluation of character image database and its application. In Proceedings of the International Conference on Document Analysis and Recognition. 1414–1418.

Digital Library

[106]

Q. A. Bui, D. Molard, and S. Tabbone. 2018. Predicting mobile-captured document images sharpness quality. In Proceedings of the International Workshop on Document Analysis Systems. 275–280. DOI:

[107]

S. Xu, S. Jiang, and W. Min. 2017. No-reference/blind image quality assessment: A survey. IETE Technical Review 34, 3 (2017), 223–245. DOI:

[108]

Z. Wang and A. C. Bovik (Eds.). 2006. Modern image quality assessment. In Synthesis Lectures on Image, Video, and Multimedia Processing. Springer, 1--16.

[109]

T. Tao, L. Ding, and H. Huang. 2020. Unified non-uniform scale adaptive sampling model for quality assessment of natural and screen images. Neurocomputing 399 (2020), 96–106.

[110]

Y. Fang, R. Du, Y. Zuo, W. Wen, and L. Li. 2020. Perceptual quality assessment for screen content images by spatial continuity. IEEE Transactions on Circuits and Systems for Video Technology 30, 11 (2020), 4050–4063. DOI:

[111]

K. Gu, J. Zhou, J.-F. Qiao, G. Zhai, W. Lin, and A. C. Bovik. 2017. No-reference quality assessment of screen content pictures. IEEE Transactions on Image Processing 26, 8 (2017), 4005–4018. DOI:

Digital Library

[112]

P. Ye, J. Kumar, L. Kang, and D. Doermann. 2013. Real-time no-reference image quality assessment based on filter learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 987–994. DOI:

Digital Library

[113]

D. Schulz, J. Maureira, J. Tapia, and C. Busch. 2022. Identity Documents Image Quality Assessment. In Proceedings of the 30th European Signal Processing Conference. 1017–1021. DOI:

[114]

S. González and J. Tapia. 2022. Towards refining id cards presentation attack detection systems using face quality index. In Proceedings of the 30th European Signal Processing Conference. 1027–1031. DOI:

[115]

Z. Li, C. Yang, Q. Shen, and S. Wen. 1828. A document image dataset for quality assessment. Journal of Physics: Conference Series 1 (1828), 012033. DOI:

[116]

A. Antonacopoulos, C. Clausner, C. Papadopoulos, and S. Pletschacher. 2011. Historical document layout analysis competition. In Proceedings of the 2011 International Conference on Document Analysis and Recognition. 1516–1520.

Digital Library

[117]

N. Nayef, M. M. Luqman, S. Prum, S. Eskenazi, J. Chazalon, and J.-M. Ogier. 2015. SmartDoc-QA: A dataset for quality assessment of smartphone captured document images-single and multiple distortions. In Proceedings of the International Conference on Document Analysis and Recognition. 1231–1235.

Digital Library

[118]

H. R. Sheikh, Z. Wang, L. K. Cormack, and A. C. Bovik. 2004. LIVE image quality assessment database release 2. [Online]. Available: http://live.ece.utexas.edu/research/quality.

[119]

E. C. Larson and D. M. Chandler. 2010. Most apparent distortion: Full-reference image quality assessment and the role of strategy. Journal of Electronic Imaging 19, 1 (2010), 011006.

[120]

N. Ponomarenko et al. 2009. TID2008 - A database for evaluation of full-reference visual quality assessment metrics. Advances of Modern Radioelectronics 10 (2009), 30–45.

[121]

A. Alaei, R. Raveaux, and D. Conte. 2017. Image quality assessment based on regions of interest. Signal Image Video Process 11, 4 (2017), 673–680. DOI:

[122]

M. Darwiche, T.-A. Pham, and M. Delalandre. 2015. Comparison of JPEG's competitors for document images. In Proceedings of the 2015 International Conference on Image Processing Theory, Tools and Applications. 487–493. DOI:

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Kumar Tyagi RGoswami GVarun Somanna M(2024)Assessment of Image Blurriness in Different Compression Formats2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC)10.1109/ICOCWC60930.2024.10470872(1-5)Online publication date: 29-Jan-2024
https://doi.org/10.1109/ICOCWC60930.2024.10470872
Hauser DBeckmann MKoliander GStiehl H(2024)On Image Processing and Pattern Recognition for Thermograms of Watermarks in Manuscripts – A First Proof-of-ConceptDocument Analysis and Recognition - ICDAR 202410.1007/978-3-031-70543-4_6(91-107)Online publication date: 30-Aug-2024
https://dl.acm.org/doi/10.1007/978-3-031-70543-4_6

Index Terms

Document Image Quality Assessment: A Survey
1. General and reference
  1. Document types
    1. Surveys and overviews

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cover image ACM Computing Surveys

ACM Computing Surveys Volume 56, Issue 2

February 2024

974 pages

EISSN:1557-7341

DOI:10.1145/3613559

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Albert Zomaya
University of Sydney, Australia

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Publication History

Published: 14 September 2023

Online AM: 30 June 2023

Accepted: 22 June 2023

Revised: 12 June 2023

Received: 21 July 2021

Published in CSUR Volume 56, Issue 2

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Kumar Tyagi RGoswami GVarun Somanna M(2024)Assessment of Image Blurriness in Different Compression Formats2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC)10.1109/ICOCWC60930.2024.10470872(1-5)Online publication date: 29-Jan-2024
https://doi.org/10.1109/ICOCWC60930.2024.10470872
Hauser DBeckmann MKoliander GStiehl H(2024)On Image Processing and Pattern Recognition for Thermograms of Watermarks in Manuscripts – A First Proof-of-ConceptDocument Analysis and Recognition - ICDAR 202410.1007/978-3-031-70543-4_6(91-107)Online publication date: 30-Aug-2024
https://dl.acm.org/doi/10.1007/978-3-031-70543-4_6

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