SEMI-SUPERVISED APPROACH TO TRAIN CAPTCHA LETTER POSITION DETETOR

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F21%3APU140619" target="_blank" >RIV/00216305:26220/21:PU140619 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
SEMI-SUPERVISED APPROACH TO TRAIN CAPTCHA LETTER POSITION DETETOR
Original language description
Common Optical Character Recognition (OCR) methods benefit from the fact, that the text is distributed in images in a predictable pattern. This is not the situation with CAPTCHA systems. Utilizing OCR algorithms to overcome common web anti-abuse CAPTCHA systems is therefore a challenging task. To train a system to overcome any CAPTCHA scheme, an attacker needs a huge dataset of annotated images. And for some methods, the attacker needs not only the right answers but also an exact position of the character in the CAPTCHA image. Annotate the positions of the object in an image is a time-consuming task. In this paper, we propose a system, which can help to annotate the position of CAPTCHA character with minimal human interaction. After annotating a small sample of targeted CAPTCHA images, a YOLO-based region detection deep network is used to search for the characters’ locations.
Czech name
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Czech description
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Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of the 27nd Conference STUDENT EEICT 2018
ISBN
978-80-214-5942-7
ISSN
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e-ISSN
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Number of pages
5
Pages from-to
436-440
Publisher name
Vysoké učené Technické, Fakulta elektrotechniky a komunikačních technologií
Place of publication
Brno
Event location
Brno
Event date
Apr 27, 2021
Type of event by nationality
CST - Celostátní akce
UT code for WoS article
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