Methods and models in economy
Scientometrics and management science
Recognition of images
T. S. Chernov Glare detection and filtering in document recognition tasks on mobile devices
Methodological problems of the system analysis
T. S. Chernov Glare detection and filtering in document recognition tasks on mobile devices


This paper addresses the negative impact of glare caused by the reflection of light from the document’s protective film on various stages of Russian citizen passport recognition on mobile devices. Methods for glare detection and removal on input image are proposed with computational complexity allowing their real-time use on mobile devices. Experimental results on Russian citizen passport dataset with heavy glare presence are shown, demonstrating that glare detection and filtering significantly reduce rejection ratio of document detection subsystem based on Viola-Jones method, thus improving overall accuracy of document fields recognition.


document recognition, spatial document image quality assessment, glare

PP. 66-74.


1. Hiromichi Fujisawa. “Forty Years of Research in Character and Document Recognition-an Industrial Perspective”. In: Pattern Recognition 41.8 (Aug. 2008), pp. 2435–2446.
2. V. Arlazarov et al. “Analiz osobennostei ispolzovaniya stacionarnyh i mobilnyh malorazmernyh cifrovyh video kamer dlya raspoznavaniya dokumentov”. in: Informacionnye
tekhnologii i vychislitelnie systemy 3 (2014). “Problemy raspoznavaniya mashinochitaemyh zon s ispolzovaniem maloformatnih cif
3. K. Bulatov et al “Problems of machine-readable zone recognition captured with digital mobile cameras”. In: Trudy Instituta Sistemnogo Analiza Rossiiskoi Akademii Nauk 65.3 (2015).
4. Alessandro Artusi, Francesco Banterle and Dmitry Chetverikov. “A Survey of Specularity Removal Methods”. In: Computer Graphics Forum (2011).
5. A. C. Kokaram. “On missing data treatment for degraded video and film archives: a survey and a new Bayesian approach”. In: IEEE Transactions on Image Processing 13.3 (March 2004), pp. 397– 415.
6. Timofey S. Chernov, Dmitry P. Nikolaev and Vitali M. Kliatskine. A method of periodic pattern localization on document images. 2015.
7. H. Kim et al “Specular Reflection Separation Using Dark Channel Prior”. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition. Jun 2013, pp. 1460–1467.
8. Qingxiong Yang, Shengnan Wang and Narendra Ahuja. “Real-time Specular Highlight Removal Using Bilateral Filtering”. In: Proceedings of the 11th European Conference on Computer Vision: Part IV. ECCV’10. Heraklion, Crete, Greece: Springer-Verlag, 2010, pp. 87–100.
9. T. Nguyen et al “A novel and effective method for specular detection and removal by tensor voting”. In: 2014 IEEE International Conference on Image Processing (ICIP). Oct. 2014, pp. 1061–1065.
10. Koray Kayabol, Ercan E. Kuruoglu and Bulent Sankur. “Image Source Separation Using Color Channel Dependencies”. In: Independent Component Analysis and Signal Separation:
8th International Conference, ICA 2009, Paraty, Brazil, March 15-18, 2009. Proceedings. Springer Berlin Heidelberg, 2009, pp. 499–506.
11. Ramesh Raskar et al “Glare Aware Photography: 4D Ray Sampling for Reducing Glare Effects of Camera Lenses”. In: ACM Transactions on Graphics 27.3 (Aug. 2008), 56:1–56:10.
12. Chil-Suk Cho, Joongseok Song and Jong-Il Park. “Glare region detection in night scene using multi-layering”. In: The Third International Conference on Digital Information Processing and Communications. The Society of Digital Information and Wireless Communication. 2013,pp. 467–469.
13. Mohamed Shehata et al “Real Time Static Glare Identification in ITS”. In: Practical Real World Technologies for Communications and Embedded Platforms. 2006.
14. Zhaofeng He et al “Toward Accurate and Fast Iris Segmentation for Iris Biometrics”. In: IEEE Trans. Pattern Anal. Mach. Intell. 31.9 (Sep. 2009), pp. 1670–1684.
15. A. Madooei and M. S. Drew. “Detecting specular highlights in dermatological images”. In: 2015 IEEE International Conference on Image Processing (ICIP). Sep. 2015, pp. 4357–4360.
16. Maria Joao M. Vasconcelos and Lu??s Rosado. “Automatic Reflection Detection on Dermatological Images Acquired via Mobile Devices”. In: MIUA. 2014, pp. 85–90.
17. Danail Stoyanov and Guang-Zhong Yang. “Removing specular reflection components for robotic assisted laparoscopic surgery.” In: ICIP (3). IEEE, 2005, pp. 632–635.
18. S. Tchoulack, J. M. Pierre Langlois and F. Cheriet. “A video stream processor for real-time detection and correction of specular reflections in endoscopic images”. In: 2008 Joint 6th International IEEE Northeast Workshop on Circuits and Systems and TAISA Conference. Jun 2008, pp. 49–52.
19. Thomas Stehle. “Removal of Specular Reflections in Endoscopic Images”. In: Acta Polytechnica: Journal of Advanced Engineering 46.4 (2006), pp. 32–36.
20. G. Karapetyan and H. Sarukhanyan. “Automatic detection and concealment of specular reflections for endoscopic images”. In: Ninth International Conference on Computer Science and Information Technologies Revised Selected Papers. Sep. 2013, pp. 1–8.
21. Mirko Arnold et al “Automatic Segmentation and Inpainting of Specular Highlights for Endoscopic Imaging”. In: Journal on Image and Video Processing 2010 (Jan. 2010), 9:1–9:12.
22. J. J. Guo et al “A Specular Reflection Suppression Method for Endoscopic Images”. In: 2016 IEEE Second International Conference on Multimedia Big Data (BigMM). Apr. 2016, pp. 125–128.
23. A. Das, A. Kar and D. Bhattacharyya. “Elimination of specular reflection and identification of ROI: The first step in automated detection of Cervical Cancer using Digital Colposcopy”. In: 2011 IEEE International Conference on Imaging Systems and Techniques. May 2011, pp. 237–241.
24. Holger Lange. Automatic glare removal in reflectance imagery of the uterine cervix. 2005.
25. JungHwan Oh et al “Informative frame classification for endoscopy video”. In: Medical Image Analysis 11.2 (2007), pp. 110–127.
26. Ronald C Reitan. Automated image quality control. US Patent 5,600,574. 1997.
27. Robert A Proudfoot and Marc Levoy. Systems and methods for glare removal using polarized filtering in document scanning. US Patent 8,174,739. 2012.
28. Konstantin Bocharov and Mikhail Kostyukov. Detecting glare in a frame of image data. US Patent App. 14/564,424. 2014.
29. P. Viola and M. Jones. “Rapid object detection using a boosted cascade of simple features”. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001. I-511-I-518 vol.1.3
30. S. Usilin et al “Visual appearance based document image classification”. In: 2010 IEEE International Conference on Image Processing. Sep. 2010, pp. 2133–2136.
31. M. Agrawal and D. Doermann. “Re-targetable OCR with Intelligent Character Segmentation”. In: 2008 The Eighth IAPR International Workshop on Document Analysis Systems. Sep. 2008, pp. 183–190.



© ФИЦ ИУ РАН 2008-2018. Создание сайта "РосИнтернет технологии".