Biblio

Although several different attacks or modern security mechanisms have been proposed, the captchas created by the numbers and the letters are still used by some websites or applications to protect their information security. The reason is that the labels of the captcha data are difficult to collect for the attacker, and protector can easily control the various parameters of the captchas: like the noise, the font type, the font size, and the background color, then make this security mechanism update with the increased attack methods. It can against attacks in different situations very effectively. This paper presents a method to recognize the different text-based captchas based on a system constituted by the denoising autoencoder and the Convolutional Recurrent Neural Network (CRNN) model with the Connectionist Temporal Classification (CTC) structure. We show that our approach has a better performance for recognizing, and it solves the identification problem of indefinite character length captchas efficiently.

Engineering drawings such as Piping and Instrumentation Diagrams contain a vast amount of text data which is essential to identify shapes, pipeline activities, tags, amongst others. These diagrams are often stored in undigitised format, such as paper copy, meaning the information contained within the diagrams is not readily accessible to inspect and use for further data analytics. In this paper, we make use of the benefits of recent deep learning advances by selecting models for both text detection and text recognition, and apply them to the digitisation of text from within real world complex engineering diagrams. Results show that 90% of text strings were detected including vertical text strings, however certain non text diagram elements were detected as text. Text strings were obtained by the text recognition method for 86% of detected text instances. The findings show that whilst the chosen Deep Learning methods were able to detect and recognise text which occurred in simple scenarios, more complex representations of text including those text strings located in close proximity to other drawing elements were highlighted as a remaining challenge.

As the accessibility of high-resolution smartphone camera has increased and an improved computational speed, it is now convenient to build Business Card Readers on mobile phones. The project aims to design and develop a Business Card Reader (BCR) Application for iOS devices, using an open-source OCR Engine - Tesseract. The system accuracy was tested and evaluated using a dataset of 55 digital business cards obtained from an online repository. The accuracy result of the system was up to 74% in terms of both text recognition and data detection. A comparative analysis was carried out against a commercial business card reader application and our application performed vastly reasonable.

Online web service providers generally protect themselves through CAPTCHA. A CAPTCHA is a type of challenge-response test used in computing as an attempt to ensure that the response is generated by a person. CAPTCHAS are mainly instigated as distorted text which the handler must correctly transcribe. Numerous schemes have been proposed till date in order to prevent attacks by Bots. This paper also presents a cross correlation based approach in breaking of famous service provider's text CAPTCHA i.e. PayPal.com and the other one is of India's most visited website IRCTC.co.in. The procedure can be fragmented down into 3 firmly tied tasks: pre-processing, segmentation, and classification. The pre-processing of the image is performed to remove all the background noise of the image. The noise in the CAPTCHA are unwanted on pixels in the background. The segmentation is performed by scanning the image for on pixels. The organization is performed by using the association values of the inputs and templates. Two types of templates have been used for classification purpose. One is the standard templates which give 30% success rate and other is the noisy templates made from the captcha images and success rate achieved with these is 100%.

We regularly use communication apps like Facebook and WhatsApp on our smartphones, and the exchange of media, particularly images, has grown at an exponential rate. There are over 3 billion images shared every day on Whatsapp alone. In such a scenario, the management of images on a mobile device has become highly inefficient, and this leads to problems like low storage, manual deletion of images, disorganization etc. In this paper, we present a solution to tackle these issues by automatically classifying every image on a smartphone into a set of predefined categories, thereby segregating spam images from them, allowing the user to delete them seamlessly.

Once we had tried to propose an unbreakable CAPTCHA and we reached a result that limitation of time is effect to prevent computers from recognizing characters accurately while computers can finally recognize all text-based CAPTCHA in unlimited time. One of the existing usual ways to prevent computers from recognizing characters is distortion, and adding noise is also effective for the prevention. However, these kinds of prevention also make recognition of characters by human beings difficult. As a solution of the problems, an effective text-based CAPTCHA algorithm with amodal completion was proposed by our team. Our CAPTCHA causes computers a large amount of calculation costs while amodal completion helps human beings to recognize characters momentarily. Our CAPTCHA has evolved with aftereffects and combinations of complementary colors. We evaluated our CAPTCHA with deep learning which is attracting the most attention since deep learning is faster and more accurate than existing methods for recognition with computers. In this paper, we add jagged lines to edges of characters since edges are one of the most important parts for recognition in deep learning. In this paper, we also evaluate that how much the jagged lines decrease recognition of human beings and how much they prevent computers from the recognition. We confirm the effects of our method to deep learning.

We always use CAPTCHA(Completely Automated Public Turing test to Tell Computers and Humans Apart) to prevent automated bot for data entry. Although there are various kinds of CAPTCHAs, text-based scheme is still applied most widely, because it is one of the most convenient and user-friendly way for daily user [1]. The fact is that segmentations of different types of CAPTCHAs are not always the same, which means one of CAPTCHA's bottleneck is the segmentation. Once we could accurately split the character, the problem could be solved much easier. Unfortunately, the best way to divide them is still case by case, which is to say there is no universal way to achieve it. In this paper, we present a novel algorithm to achieve state-of-the-art performance, what was more, we also constructed a new convolutional neural network as an add-on recognition part to stabilize our state-of-the-art performance of the whole CAPTCHA system. The CAPTCHA datasets we are using is from the State Administration for Industry& Commerce of the People's Republic of China. In this datasets, there are totally 33 entrances of CAPTCHAs. In this experiments, we assume that each of the entrance is known. Results are provided showing how our algorithms work well towards these CAPTCHAs.

In this paper we explore the potential for securing a distributed Arabic Optical Character Recognition (OCR) system via cloud computing technology in a pervasive and mobile environment. The goal of the system is to achieve full accuracy, high speed and security when taking into account large vocabularies and amounts of documents. This issue has been resolved by integrating the recognition process and the security issue with multiprocessing and distributed computing technologies.