Recognition of Multi-Fontstyle Characters Based on Convolutional Neural Network

Lv, Gang

doi:10.1109/iscid.2011.157

Cited by 11 publications

(2 citation statements)

References 4 publications

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“…Levellines used DCNN for classification of a small data set of Baidu CAPTCHA. Thus, it gained an accuracy of 98.4% [23]. As mentioned earlier, Amodal CAPTCHA is resistant to conventional threats.…”

Section: Evaluation Of Amodal Captcha Using Dcnnsmentioning

confidence: 72%

Evaluation of Ergonomically Designed CAPTCHAs using Deep Learning Technology

Azakami

Shibata

Uda

2018

Journal of Information Processing

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Although most existing text-based CAPTCHAs use distorted images of alphanumerics, they have been criticized because large image distortions make it difficult for human beings to recognize the characters, despite the ease with which computers can eliminate distortions and consequently recognize them. Ergonomically designed CAPTCHAs, which exploit human-specific phenomena, are a solution to this problem. Ergonomic design enables humans to momentarily recognize them, while significantly increasing computational costs for machines to recognize characters. Recently, owing to the development of deep learning, the image recognition capability of machines has improved dramatically. Characters are easily recognized within reasonable time by deep convolutional neural networks (DCNNs), which have similar architectures to visual perception mechanisms of the brain. In this paper, to clarify whether ergonomically designed CAPTCHAs can withstand state-of-the-art methods of deep learning, we use several kinds of DCNNs to measure the classification rates of the characters displayed. With respect to ergonomic designs, we first use Amodal CAPTCHA proposed by Mori et al., which exploits the two human-specific phenomena of amodal completion and aftereffects. We secondly modify Amodal CAPTCHA by adding jagged lines to the edges of characters, aiming to prevent DCNNs from recognizing them correctly, since edges are one of the most fundamental features for DCNNs. Experimental results, however, show that both naive and jagged-lined Amodal CAPTCHAs are almost completely broken. Another approach we conducted is to use only complete characters without shielding as training data, assuming that attackers have no information about how amodal completion and jagged edges were applied. However, even for this assumption, the classification rate of DCNNs is still sufficiently high. On the whole, our results in this paper show that any ergonomic effects such as amodal completion and jagged edges are no longer countermeasures against character recognition by DCNNs.

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Section: Evaluation Of Amodal Captcha Using Dcnnsmentioning

confidence: 72%

Evaluation of Ergonomically Designed CAPTCHAs using Deep Learning Technology

Azakami

Shibata

Uda

2018

Journal of Information Processing

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“…The networks incorporate constraints and achieve some degree of shift and deformation invariance. This method has demonstrated to be successful in various fields such as character recognition [21], document recognition [22], object recognition [23], handwritten digit recognition [24], EEG signal classification [25], and facial expression recognition [26]. However, the conventional CNNs are currently limited to handling video-based images.…”

Section: Introductionmentioning

confidence: 99%

Facial Expression Recognition Using 3D Convolutional Neural Network

Byeon¹,

Kwak²

2014

ijacsa

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Abstract-This paper is concerned with video-based facial expression recognition frequently used in conjunction with HRI (Human-Robot Interaction) that can naturally interact between human and robot. For this purpose, we design a 3D-CNN(3D Convolutional Neural Networks) by augmenting dimensionality reduction methods such as PCA(Principal Component Analysis) and TMPCA(Tensor-based Multilinear Principal Component Analysis) to recognize simultaneously the successive frames with facial expression images obtained through video camera. The 3D-CNN can achieve some degree of shift and deformation invariance using local receptive fields and spatial subsampling through dimensionality reduction of redundant CNN's output. The experimental results on video-based facial expression database reveal that the presented method shows a good performance in comparison to the conventional methods such as PCA and TMPCA.

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CNN-Based Chinese Character Recognition with Skeleton Feature

Tang

et al. 2018

Lecture Notes in Computer Science

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Recognition of Multi-Fontstyle Characters Based on Convolutional Neural Network

Cited by 11 publications

References 4 publications

Evaluation of Ergonomically Designed CAPTCHAs using Deep Learning Technology

Evaluation of Ergonomically Designed CAPTCHAs using Deep Learning Technology

Facial Expression Recognition Using 3D Convolutional Neural Network

CNN-Based Chinese Character Recognition with Skeleton Feature

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