A security analysis of automated Chinese turing tests

Text-based Captchas have been widely used to deter misuse of services on the Internet. However, many designs have been broken. It is intellectually interesting and practically relevant to look for alternative designs, which are currently a topic of active research. We motivate the study of Chinese Captchas as an interesting alternative design - counterintuitively, it is possible to design Chinese Captchas that are universally usable, even to those who have never studied Chinese language. More importantly, we ask a fundamental question: is the segmentation-resistance principle established for Roman-character based Captchas applicable to Chinese based designs? With deep learning techniques, we offer the first evidence that computers do recognize individual Chinese characters well, regardless of distortion levels. This suggests that many real-world Chinese schemes are insecure, in contrast to common beliefs. Our result offers an essential guideline to the design of secure Chinese Captchas, and it is also applicable to Captchas using other large-alphabet languages such as Japanese

Exploiting zoning based on approximating splines in cursive script recognition

Because of its complexity, handwriting recognition has to exploit many sources of information to be successful, e.g. the handwriting zones. Variability of zone-lines, however, requires a more flexible representation than traditional horizontal or linear methods. The proposed method therefore employs approximating cubic splines. Using entire lines of text rather than individual words is shown to improve the zoning accuracy, especially for short words. The new method represents an improvement over existing methods in terms of range of applicability, zone-line precision and zoning-classification accuracy. Application to several problems of handwriting recognition is demonstrated and evaluated

Evaluating the usability and security of a video CAPTCHA

A CAPTCHA is a variation of the Turing test, in which a challenge is used to distinguish humans from computers (`bots\u27) on the internet. They are commonly used to prevent the abuse of online services. CAPTCHAs discriminate using hard articial intelligence problems: the most common type requires a user to transcribe distorted characters displayed within a noisy image. Unfortunately, many users and them frustrating and break rates as high as 60% have been reported (for Microsoft\u27s Hotmail). We present a new CAPTCHA in which users provide three words (`tags\u27) that describe a video. A challenge is passed if a user\u27s tag belongs to a set of automatically generated ground-truth tags. In an experiment, we were able to increase human pass rates for our video CAPTCHAs from 69.7% to 90.2% (184 participants over 20 videos). Under the same conditions, the pass rate for an attack submitting the three most frequent tags (estimated over 86,368 videos) remained nearly constant (5% over the 20 videos, roughly 12.9% over a separate sample of 5146 videos). Challenge videos were taken from YouTube.com. For each video, 90 tags were added from related videos to the ground-truth set; security was maintained by pruning all tags with a frequency 0.6%. Tag stemming and approximate matching were also used to increase human pass rates. Only 20.1% of participants preferred text-based CAPTCHAs, while 58.2% preferred our video-based alternative. Finally, we demonstrate how our technique for extending the ground truth tags allows for different usability/security trade-offs, and discuss how it can be applied to other types of CAPTCHAs

Optimizing digital archiving: An artificial intelligence approach for OCR error correction

Project Work presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business AnalyticsThis thesis research scopes the knowledge gap for effective ways to address OCR errors and the importance to have training datasets adequated size and quality, to promote digital documents OCR recognition efficiency. The main goal is to examine the effects regarding the following dimensions of sourcing data: input size vs performance vs time efficiency, and to propose a new design that includes a machine translation model, to automate the errors correction caused by OCR scan. The study implemented various LSTM, with different thresholds, to recover errors generated by OCR systems. However, the results did not overcomed the performance of existing OCR systems, due to dataset size limitations, a step further was achieved. A relationship between performance and input size was established, providing meaningful insights for future digital archiving systems optimisation. This dissertation creates a new approach, to deal with OCR problems and implementation considerations, that can be further followed, to optimise digital archive systems efficiency and results

On the typography of flight-deck documentation

Many types of paper documentation are employed on the flight-deck. They range from a simple checklist card to a bulky Aircraft Flight Manual (AFM). Some of these documentations have typographical and graphical deficiencies; yet, many cockpit tasks such as conducting checklists, way-point entry, limitations and performance calculations, and many more, require the use of these documents. Moreover, during emergency and abnormal situations, the flight crews' effectiveness in combating the situation is highly dependent on such documentation; accessing and reading procedures has a significant impact on flight safety. Although flight-deck documentation are an important (and sometimes critical) form of display in the modern cockpit, there is a dearth of information on how to effectively design these displays. The object of this report is to provide a summary of the available literature regarding the design and typographical aspects of printed matter. The report attempts 'to bridge' the gap between basic research about typography, and the kind of information needed by designers of flight-deck documentation. The report focuses on typographical factors such as type-faces, character height, use of lower- and upper-case characters, line length, and spacing. Some graphical aspects such as layout, color coding, fonts, and character contrast are also discussed. In addition, several aspects of cockpit reading conditions such as glare, angular alignment, and paper quality are addressed. Finally, a list of recommendations for the graphical design of flight-deck documentation is provided

Feature recognition in OCR text

This thesis investigates the recognition and extraction of special word sequences, representing concepts, from OCR text. Unlike general index terms, concepts can consist of one or more terms that combined, have higher retrieval value than the terms alone (i.e. acronyms, proper nouns, phrases). An algorithm to recognize acronyms and their definitions will be presented. An evaluation of the algorithm will also be presented

Batch-adaptive rejection threshold estimation with application to OCR post-processing

An OCR process is often followed by the application of a language model to find the best transformation of an OCR hypothesis into a string compatible with the constraints of the document, field or item under consideration. The cost of this transformation can be taken as a confidence value and compared to a threshold to decide if a string is accepted as correct or rejected in order to satisfy the need for bounding the error rate of the system. Widespread tools like ROC, precision-recall, or error-reject curves, are commonly used along with fixed thresholding in order to achieve that goal. However, those methodologies fail when a test sample has a confidence distribution that differs from the one of the sample used to train the system, which is a very frequent case in post-processed OCR strings (e.g., string batches showing particularly careful handwriting styles in contrast to free styles). In this paper, we propose an adaptive method for the automatic estimation of the rejection threshold that overcomes this drawback, allowing the operator to define an expected error rate within the set of accepted (non-rejected) strings of a complete batch of documents (as opposed to trying to establish or control the probability of error of a single string), regardless of its confidence distribution. The operator (expert) is assumed to know the error rate that can be acceptable to the user of the resulting data. The proposed system transforms that knowledge into a suitable rejection threshold. The approach is based on the estimation of an expected error vs. transformation cost distribution. First, a model predicting the probability of a cost to arise from an erroneously transcribed string is computed from a sample of supervised OCR hypotheses. Then, given a test sample, a cumulative error vs. cost curve is computed and used to automatically set the appropriate threshold that meets the user-defined error rate on the overall sample. The results of experiments on batches coming from different writing styles show very accurate error rate estimations where fixed thresholding clearly fails. An original procedure to generate distorted strings from a given language is also proposed and tested, which allows the use of the presented method in tasks where no real supervised OCR hypotheses are available to train the system.Navarro Cerdan, JR.; Arlandis Navarro, JF.; Llobet Azpitarte, R.; Perez-Cortes, J. (2015). Batch-adaptive rejection threshold estimation with application to OCR post-processing. Expert Systems with Applications. 42(21):8111-8122. doi:10.1016/j.eswa.2015.06.022S81118122422