Generation and use of handwritten CAPTCHAs

Automated recognition of unconstrained handwriting continues to be a challenging research task. In contrast to the traditional role of handwriting recognition in applications such as postal automation and bank check reading, in this paper, we explore the use of handwriting recognition in designing CAPTCHAs for cyber security. CAPTCHAs (Completely Automatic Public Turing tests to tell Computers and Humans Apart) are automatic reverse Turing tests designed so that virtually all humans can pass the test, but state-of-the-art computer programs will fail. Machine-printed, text-based CAPTCHAs are now commonly used to defend against bot attacks. Our focus is on exploring the generation and use of handwritten CAPTCHAs. We have used a large repository of handwritten word images that current handwriting recognizers cannot read (even when provided with a lexicon) for this purpose and also used synthetic handwritten samples. We take advantage of both our knowledge of the common source of errors in automated handwriting recognition systems as well as the salient aspects of human reading. The simultaneous interplay of several Gestalt laws of perception and the geon theory of pattern recognition (that implies object recognition occurs by components) allows us to explore the parameters that truly separate human and machine abilities.     

Segmentation of connected characters in text-based CAPTCHAs for intelligent character recognition

Over last few years, CAPTCHAs are ubiquitously found on internet as a security mechanism to distinguish between humans and spams. The text-based CAPTCHAs offer users to recognize the distorted text from the challenged images. Having based on hard AI problem, they have emerged as a hot research topic in computer vision and machine learning. The contemporary text-based CAPTCHAs are based on the segmentation problem that involves their decomposition into sub-images of individual characters. This is a challenging task for current OCR programs which is not yet solved to a great extent. In this paper, we present a novel segmentation and recognition method which uses simple image processing techniques including thresholding, thinning and pixel count methods along with an artificial neural network for text-based CAPTCHAs. We attack the popular CCT (Crowded Characters Together) based CAPTCHAs and compare our results with other schemes. As overall, our system achieves an overall precision of 51.3, 27.1 and 53.2% for Taobao, MSN and eBay datasets with 1000,500 and 1000 CAPTCHAs respectively. The benefits of this research are twofold: by recognizing text-based CAPTCHAs, we not only explore the weaknesses in the current design but also find a way to segment and recognize the connected characters from images. The proposed algorithm can be used in digitization of ancient books, handwriting recognition and other similar tasks.     

CAPTCHAs: Humans vs. Bots

A completely automated public turing test to tell computers and humans apart (CAPT-CHA) offers a way for Web service providers to make some conclusions about whether a "user" is human or robot. Process of CAPTCHA recognition is a combination of efforts, approaches, and software that attempts to increase accuracy to an acceptable level. Of course, it's hard to define this level, but we've found that it often starts from 5 percent accuracy. We've covered the most popular CAPTCHA approaches here but not all of them. Most users know how to use the more commonly used graphical CAPTCHAs, so designers must have serious reasons to migrate to other, more difficult-to-break, approaches. After all, CAPTCHAs aim to determine legitimate users, not alienate them.     

Towards Written Text Recognition Based on Handwriting Experiences Using a Recurrent Neural Network

In this paper, we propose a model for recognizing written text through prediction of a handwriting sequence. The approach is based on findings in the brain sciences field. When recognizing written text, humans are said to unintentionally trace its handwriting sequence in their brains. Likewise, we aim to create a model that predicts a handwriting sequence from a static image of written text. The predicted handwriting sequence would be used to recognize the text. As the first step towards the goal, we created a model using neural networks, and evaluated the learning and recognition capability of the model using single Japanese characters. First, the handwriting image sequences for training are self-organized into image features using a self-organizing map. The self-organized image features are used to train the neuro-dynamics learning model. For recognition, we used both trained and untrained image sequences to evaluate the capability of the model to adapt to unknown data. The results of two experiments using 10 Japanese characters show the effectivity of the model.     

Using data mining for digital ink recognition: Dividing text and shapes in sketched diagrams

The low accuracy rates of text-shape dividers for digital ink diagrams are hindering their use in real world applications. While recognition of handwriting is well advanced and there have been many recognition approaches proposed for hand drawn sketches, there has been less attention on the division of text and drawing ink. Feature based recognition is a common approach for text-shape division. However, the choice of features and algorithms are critical to the success of the recognition. We propose the use of data mining techniques to build more accurate text-shape dividers. A comparative study is used to systematically identify the algorithms best suited for the specific problem. We have generated dividers using data mining with diagrams from three domains and a comprehensive ink feature library. The extensive evaluation on diagrams from six different domains has shown that our resulting dividers, using LADTree and LogitBoost, are significantly more accurate than three existing dividers.     

An approach for real-time recognition of online Chinese handwritten sentences

With the advances of handwriting capturing devices and computing power of mobile computers, pen-based Chinese text input is moving from character-based input to sentence-based input. This paper proposes a real-time recognition approach for sentence-based input of Chinese handwriting. The main feature of the approach is a dynamically maintained segmentation–recognition candidate lattice that integrates multiple contexts including character classification, linguistic context and geometric context. Whenever a new stroke is produced, dynamic text line segmentation and character over-segmentation are performed to locate the position of the stroke in text lines and update the primitive segment sequence of the page. Candidate characters are then generated and recognized to assign candidate classes, and linguistic context and geometric context involving the newly generated candidate characters are computed. The candidate lattice is updated while the writing process continues. When the pen lift time exceeds a threshold, the system searches the candidate lattice for the result of sentence recognition. Since the computation of multiple contexts consumes the majority of computing and is performed during writing process, the recognition result is obtained immediately after the writing of a sentence is finished. Experiments on a large database CASIA-OLHWDB of unconstrained online Chinese handwriting demonstrate the robustness and effectiveness of the proposed approach.     

A method for automatic classification of gender based on text- independent handwriting

Handwriting recognition is used for the prediction of various demographic traits such as age, gender, nationality, etc. Out of all the applications gender prediction is mainly admired topic among researchers. The relation between gender and handwriting can be seen from the physical appearance of the handwriting. This research work predicts gender from handwriting using the landmarks of differences between the two genders. We use the shape or visual appearance of the handwriting for extracting features of the handwriting such as slanteness (direction), area (no of pixels occupied by text), perimeter (length of edges), etc. Classification is carried out using the Support Vector Machine (SVM) as a classifier which transforms the nonlinear problem into linear using its kernel trick, logistic regression, KNN and at the end to enhance the classification rates we use Majority Voting. The experimental results obtained on a dataset of 282 writers with 2 samples per writer shows that the proposed method attains appealing performance on writer detection and text-independent environment.

     

CAPTCHA技术研究综述

全自动开放式人机区分图灵测试(completely automated public Turing test to tell computers and humans apart,CAPTCHA)又称为人机交互验证(human interactive proof,HIP),它能自动产生并评估一个测试,这个测试能被几乎所有人类用户通过,而现有的计算机程序不能通过.CAPTCHA提供了一种自动驱分人和机器的手段,已成为一种标准的网络安全技术成功应用于包括Google,Yahoo!以及微软在内的各大网站.CAPTCHA设计基于人工智能领域的开放性问题,按表现载体和内容不同分为文本、图像、声音3种类型,其中,基于字符识别的文本CAPTCHA已得到广泛使用;图像CAPTCHA利用计算机视觉中的难解问题,目前尚处于研究阶段;声音CAPTCHA针对视觉残障者,是对前两种视觉CAPTCHA的补充.介绍了CAPTCHA的发展和设计准则,详细阐述CAPTCHA设计和破解的研究工作及最新进展,给出典型实例,讨论其可用性和安全性,最后指出未来CAPTCHA技术的发展方向和亟待解决的问题.     

面向连续叠写的高精简中文手写识别方法研究

连续手写识别是中文手写输入技术的核心,自然、快捷地输入中文信息一直是模式识别乃至人工智能领域追求的目标。提出了一种有效克服小屏幕限制的连续叠写汉字识别方法。该方法基于切分-识别集成的解码框架,先使用过切分算法处理输入的书写轨迹;然后启用一种新颖的感知机算法判定字符的边界;随后采用来自字符分类模型、几何模型和语言模型的多种上下文信息进行路径解码。为适应不同类型的移动终端,特别提出了一种高效压缩字符分类模型的方法,以有效减少字符识别过程对存储和内存的占用。该识别方法已在Android平台上部署,并进行了大规模的测试实验。实验结果证实了该识别方法的性能和效率。     

基于多形变特征的汉字验证码的设计及实现*

验证码技术现已成为提高网站安全的一个手段,但是由于只有简单的形变处理,当前很多应用的验证码缺乏可靠的安全性,具有很高的破解率。因此,设计了一种具有字符拥挤效果的、拥有多种变形特征的随机汉字验证码生成算法,并将其成功应用于JSP动态网页中。实验表明,产生的验证码具有更高的安全性,且易于用户使用。     

卷积神经网络识别汉字验证码

验证码今已广泛应用在各个领域,常见的英文字母与数字组合的验证码自动识别准确率已达到较高的水准,而汉字因其字符复杂,用传统方法进行自动识别难度很大。提出一种基于卷积神经网络的验证码自动识别方法来提高字符的识别准确率。采用Keras卷积神经网络框架,设计多层卷积来提取深层次图像信息,分别对汉字验证码和字母数字验证码进行识别,以提高模型的泛化性。实验结果表明用该方法汉字验证码的单字识别率已达到99.4%;传统四字符字母数字验证码的识别率最高达到99.3%。这一结果表明深度神经网络对验证码复杂结构的感知能力很强大,通过对比实验发现Keras框架在验证码识别领域有较好效果。     

Unsupervised writer adaptation applied to handwritten text recognition

This paper deals with the problem of off-line handwritten text recognition. It presents a system of text recognition that exploits an original principle of adaptation to the handwriting to be recognized. The adaptation principle is based on the automatic learning, during the recognition, of the graphical characteristics of the handwriting. This on-line adaptation of the recognition system relies on the iteration of two steps: a word recognition step that allows to label the writer's representations (allographs) on the whole text and a re-evaluation step of character models. Tests carried out on a sample of 15 writers, all unknown by the system, show the interest of the proposed adaptation scheme since we obtain during iterations an improvement of recognition rates both at the letter and the word levels.     

A Fuzzy Statistical Rule Generation Method for Handwriting Recognition

This paper presents a statistical approach for rule-base generation of handwriting recognition. The proposed method integrates the heuristic feature selection with the statistical evaluation and thus improves the performance of the rule generation as well as of the fuzzy handwriting recognition system. Fuzzy statistical measures are employed to identify relevant features from a given large handwriting database. First an automatic rule-base mechanism is presented. To reduce the time needed for this generation mechanism an additional heuristic feature selection step is introduced. Tests show that this generated rule-base improved the recognition results over previous approaches.     

Trans-Net：基于迁移学习的手写简笔画识别

手写输入可通过少量的书写进而传递丰富的文本信息,如何准确地对手写简笔画进行识别越来越引起了各界研究者们的关注。传统的简笔画识别算法多基于简笔画相对固定的结构特性进行识别。此种方法对于笔迹清晰、结构相对简单的简笔画具有较高的识别率,但是随着分类数以及简笔画自身结构复杂度的增加这种方法存在一定局限性,往往会造成误分类。为取得更好的识别效果,该研究以具有固定参照模板的简笔画作为研究对象,使用图像生成算法对手写笔迹进行预处理,并提出了一种基于卷积神经网络的简笔画识别模型（Trans-Net）,其中运用迁移学习技术解决了样本库中数据量小的问题。实验结果表明,该方法能够对输入的简笔画笔迹进行有效地特征提取,并且对样本库中150类简笔画对象的平均识别精度达到了94.1%。