Abstract
This study proposed an assistive system to recognize the special character CAPTCHAs for the visually impaired community in the Chinese region. To improve the recognition precision, a convolutional neural network (CNN), which is named Captchanet for recognition, was proposed. Firstly, a ten-layer network architecture was designed and three improved training strategies were proposed for the deep learning model. Secondly, a customized Chinese character training set was designed using a novel and fast method, with the view of overcoming the limitation in labeled data collection and uneven data distribution. Finally, the experiments were conducted on the test set gathered from public websites to test the effectiveness of the proposed Captchanet. The statistical results demonstrated that the Captchanet has better classification performance and has obtained higher success rates of recognition than the well-known machine learning approaches and CNN-based approaches.










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Meutzner, H., Kolossa, D.: Reducing the cost of breaking audio CAPTCHAs by active and semi-supervised learning. In: CMLA, pp. 67–73 (2014)
Obimbo, C., Halligan, A., Freitas, P.D.: Captchall: an improvement on the modern text-based captcha. In: ICCS, pp. 496–501 (2013)
Lv, G.: Recognition of multi-fontstyle characters based on convolutional neural network. In: ISCID, pp. 223–225 (2011)
Santosh, K.C.: Character recognition based on DTW-Radon. In: ICDAR, pp. 264–268 (2011)
Dai, F., Gao, H., Liu, D.: Breaking CAPTCHAs with second template matching and BP neural network algorithms. In: IJIM, pp. 126–133 (2013)
Fujioka, H., Zhu, W., Hidaka, A., Kano, H.: Reconstructing dynamic font-based Chinese characters using support vector machine. In: SMC, pp. 2408–2413 (2017)
Zhou, M.K., Zhang, X.Y., Yin, F., Liu, C.L.: Discriminative quadratic feature learning for handwritten Chinese character recognition. Pattern Recogn. 49, 7–18 (2016)
Wang, N., Zhu, X., Zhang, J.: License plate segmentation and recognition of Chinese vehicle based on BPNN. In: CIS, pp. 403–406 (2016)
Kim, H.J., Kim, K.H., Kim, S.K., Lee, J.K.: On-line recognition of handwritten Chinese characters based on hidden markov models. Pattern Recogn. 30(9), 1489–1500 (1997)
Lin, D., Lin, F., Lv, Y., Cai, F., Cao, D.: Chinese character CAPTCHA recognition and performance estimation via deep neural network. Neurocomputing 288, 11–19 (2018)
Tang, Y., Wu, B., Peng, L., Liu, C.: Semi-supervised transfer learning for convolutional neural network based Chinese character recognition. In: ICDAR, pp. 441–447 (2018)
Wang, Y., Huang, Y., Zheng, W., Zhou, Z., Liu, D., Lu, M.: Combining convolutional neural network and self-adaptive algorithm to defeat synthetic multi-digit text-based CAPTCHA. In: ICIT, pp. 980–985 (2017)
Ren, X., Zhou, Y., He, J., Chen, K., Yang, X., Sun, J.: A convolutional neural network based Chinese text detection algorithm via text structure modeling. IEEE Trans. Multimed. 19, 506–518 (2017)
Chen, J., Luo, X., Guo, Y., Zhang, Y., Gong, D.: A survey on breaking technique of text-based CAPTCHA. Secur. Commun. Netw. (2017). https://doi.org/10.1155/2017/6898617
Mori, G., Malik, J.: Recognizing objects in adversarial clutter: breaking a visual CAPTCHA. In: CVPR, pp. 134–141 (2003)
Santosh, K.C., Wendling, L.: Character recognition based on non-linear multi-projection profiles measure. Front. Comput. Sci. 9(5), 678–690 (2015)
Starostenko, O., Cruzperez, C., Ucedaponga, F., Alarconaquino, V.: Breaking text-based CAPTCHAs with variable word and character orientation. Pattern Recogn. 48(4), 1101–1112 (2015)
Guo, P., Deng, Y. W., Zhang, H.: A CAPTCHA image recognition algorithm based on edit distance. In: KEM, pp. 2203–2207 (2011)
Wu, X., Dai, S., Guo, Y., Fujita, H.: A machine learning attack against variable-length Chinese character CAPTCHAs. Appl. Intell. 49(4), 1548–1565 (2019)
Hussain, R., Gao, H., Shaikh, R.A.: Segmentation of connected characters in text-based CAPTCHAs for intelligent character recognition. Multimed. Tools Appl. 76(24), 25547–25561 (2017)
Rui, C., Jing, Y., Ronggui, H., Shuguang, H.: A novel LSTM-RNN decoding algorithm in CAPTCHA recognition. In: IMCCC, pp. 766–771 (2013)
Zhang, T., Zheng, H., Zhang, L.: Verification CAPTCHA based on deep learning. In: CCC, pp. 484–488 (2018)
Chen, J., Luo, X., Liu, Y., Wang, J., Ma, Y.: Selective learning confusion class for text-based CAPTCHA recognition. IEEE Access 7, 22246–22259 (2019)
Guha, R., Das, N., Kundu, M., Nasipuri, M., Santosh, K.C., Member, I.S.: DevNet: an efficient CNN architecture for handwritten Devanagari character recognition. In: IJPRAI (2019)
Song, X., Gao, X., Ding, Y., Wang, Z.: A handwritten Chinese characters recognition method based on sample set expansion and CNN. In: ICSAI, pp. 843–849 (2017)
Wang, Y., Li, X., Liu, C., Ding, X., Chen, Y.: An mqdf-cnn hybrid model for offline handwritten Chinese character recognition. In: ICFHR, pp. 246–249 (2014)
Wang, Q., Lu, Y.: Similar handwritten chinese character recognition using hierarchical CNN model. In: ICDAR, pp. 603–608 (2017)
Li, Z., Teng, N., Jin, M., Lu, H.: Building efficient cnn architecture for offline handwritten Chinese character recognition. In: IJDAR, pp. 233–240 (2018)
Wang, J., Qin, J., Xiang, X., Tan, Y., Pan, N.: CAPTCHA recognition based on deep convolutional neural network. Math. Biosci. Eng. 16(5), 5851–5861 (2019)
Hu, J., Ma, W., Khan, A., Liu, L.: Recognizing character-matching CAPTCHA using convolutional neural networks with triple loss. In: KSEM, pp. 209–220 (2018)
Liu, C., Yin, F., Wang, D., Wang, Q.: Casia online and offline Chinese handwriting databases. In: ICDAR, pp. 37–41 (2011)
Zhang, L.L., Xie, Y., Luan, X., He, J.: Captcha automatic segmentation and recognition based on improved vertical projection. In: ICCSN, pp. 1167–1172 (2017)
Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man. Cybern 9(1), 62–66 (2007)
Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. 8(6), 679–698 (1986)
Xiao, X., Jin, L., Yang, Y., Yang, W., Sun, J., Chang, T.: Building fast and compact convolutional neural networks for offline handwritten Chinese character recognition. Pattern Recogn. 72, 72–81 (2017)
Yao, D., Zhu, W., Chen, Y., Zhang, L.: Chinese license plate character recognition based on convolution neural network. In: CAC, pp. 1547–1552 (2017)
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: ICML, pp. 448–456 (2015)
Xu, B., Wang, N., Chen, T., Li, M.: Empirical evaluation of rectified activations in convolutional network. In: Computer Science (2015)
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)
Wang, S., Chen, L., Xu, L.: Deep knowledge training and heterogeneous CNN for handwritten Chinese text recognition. In: ICFHR, pp. 84–89 (2016)
Zhang, W., Li, C., Peng, G., Chen, Y., Zhang, Z.: A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load. Mech. Syst. Signal Process. 100, 439–453 (2018)
Taigman, Y., Yang, M., Ranzato, M., Wolf, L.: DeepFace: closing the gap to human-level performance in face verification. In: CVPR, pp. 1701–1708 (2014)
Mishkin, D., Sergievskiy, N., Matas, J.: Systematic evaluation of convolution neural network advances on the imagenet. Comput. Vis. Image Underst. 161, 11–19 (2017)
Needleman, M.: The Unicode standard. Serials Rev. 26(2), 51–54 (2000)
Masko, D., Hensman, P.: The impact of imbalanced training data for convolutional neural networks, pp. 12–20 (2015)
Si, L., Wang, Z., Xu, R., Tan, C., Liu, X., Xu, J.: Image enhancement for surveillance video of coal mining face based on single-scale retinex algorithm combined with bilateral filtering. Symmetry 9(6), 93 (2017)
Abu-Ain, W., Abdullah, S.N.H.S., Bataineh, B., Abu-Ain, T., Omar, K.: Skeletonization algorithm for binary images. In: Procedia Technology, pp. 704–709 (2013)
Zhang, T.Y., Suen, C.Y.: A fast parallel algorithm for thinning digital patterns. Commun. ACM 27(3), 236–239 (1984)
Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. J Mach. Learn. Res. 9, 249–256 (2010)
Boer, P.T.D., Kroese, D.P., Mannor, S., Rubinstein, R.Y.: A tutorial on the cross-entropy method. Ann. Oper. Res. 134(1), 19–67 (2005)
Powers, D.M.W.: Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. Mach. Learn. Technol. 2(1), 37–63 (2011)
Tang, Z., Jiang, W., Zhang, Z.: DenseNet with up-sampling block for recognizing texts in images. Neural Comput. Appl. 32, 7553–7561 (2020)
Zeiler, M. D., Fergus, R.: Visualizing and understanding convolutional networks. In: ECCV, pp. 818–833 (2013)
Acknowledgements
The support of National Natural Science Foundation of China (No. 51975568), National Natural Science Foundation of Jiangsu Province (No. BK20191341) and Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) in carrying out this research are gratefully acknowledged.
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Zhang, X., Liu, X., Sarkodie-Gyan, T. et al. Development of a character CAPTCHA recognition system for the visually impaired community using deep learning. Machine Vision and Applications 32, 29 (2021). https://doi.org/10.1007/s00138-020-01160-8
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DOI: https://doi.org/10.1007/s00138-020-01160-8