In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. Lee, C.Y., Osindero, S.: Recursive recurrent nets with attention modeling for OCR in the wild. In: International Joint Conference on Neural Networks (2018) Laroca, R., et al.: A robust real-time automatic license plate recognition based on the YOLO detector. In: Fuzzy Systems Association and International Conference on Soft Computing and Intelligent Systems (2017) Hurtik, P., Vajgl, M.: Automatic license plate recognition in difficult conditions - technical report. Hui Li, C.S.: Reading car license plates using deep convolutional neural networks and LSTMS (2016) (eds.) Trends in Applied Knowledge-Based Systems and Data Science, pp. In: Fujita, H., Ali, M., Selamat, A., Sasaki, J., Kurematsu, M. He, S., Yang, C., Pan, J.S.: The research of chinese license plates recognition based on CNN and length\_feature. Gou, C., Wang, K., Yao, Y., Li, Z.: Vehicle license plate recognition based on extremal regions and restricted Boltzmann machines. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), vol. 577–585 (2015)ĭing, H., et al.: A compact CNN-DBLSTM based character model for offline handwriting recognition with Tucker decomposition. In: Advances in Neural Information Processing Systems, pp. 23–27 (2017)Ĭheang, T.K., Chong, Y.S., Yong, H.T.: Segmentation-free vehicle license plate recognition using ConvNet-RNN (2017)Ĭhorowski, J.K., Bahdanau, D., Serdyuk, D., Cho, K., Bengio, Y.: Attention-based models for speech recognition. In: Proceedings of the 18th International Society for Music Information Retrieval Conference, Suzhou, China, pp. IEEE (2016)īhunia, A.K., Konwer, A., Bhowmick, A., Bhunia, A.K., Roy, P.P., Pal, U.: Script identification in natural scene image and video frame using attention based convolutional-LSTM network (2018)Ĭalvo-Zaragoza, J., Valero-Mas, J.J., Pertusa, A.: End-to-end optical music recognition using neural networks. In: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. arXiv preprint arXiv:1409.0473 (2014)īahdanau, D., Chorowski, J., Serdyuk, D., Brakel, P., Bengio, Y.: End-to-end attention-based large vocabulary speech recognition. Īngara, N.S.S.: Automatic license plate recognition using deep learning techniques (2015)īahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. In: 2015 14th IAPR International Conference on Machine Vision Applications (MVA), pp. Organizacija 50(3), 285–295 (2017)Ībtahi, F., Zhu, Z., Burry, A.M.: A deep reinforcement learning approach to character segmentation of license plate images. KeywordsĪboura, K., Al-Hmouz, R.: An overview of image analysis algorithms for license plate recognition. Experiments showed that the AC-RNN performs better on the testing real images, increasing about 5% on accuracy, compared with classic ConvNet-RNN. The AC-RNN could figure out the vehicle license even in cases of light changing, spatial distortion and partial blurry. The proposed AC-RNN was trained on a large generated dataset which contains various types of license plates in China. While the ConvNet is used to extract features, the recurrent neural networks (RNN) with connectionist temporal classification (CTC) are applied for sequence labeling. The attention mechanism helps to locate the important instances in the step of recognition. Based on the idea of Segmentation-free VLPR, this paper proposed an attention enhanced ConvNet-RNN (AC-RNN) for accurate Chinese Vehicle License Plate Recognition. Segmentation-free VLPR systems compute the label in one pass using Long Short-Term Memory Network (LSTM), without individual segmentation step, their results tend to be not influenced by the segmentation accuracy. While a lot of approaches have been proposed, and achieved good performance to some extent, these approaches still have problems, for example, in the condition of characters’ distortion or partial occlusion. As an important part of intelligent transportation system, vehicle license plate recognition requires high accuracy in an open environment.
0 Comments
Leave a Reply. |