Abstract
Writer Identification is a challenging problem based on small amount of handwritten text. It is also a foremost research topic in forensic analysis of documents. For this, a convolution neural network (CNN) is proposed to address the writer recognition task. In this work, we propose a dual-path CNN to extract local features maps from given input handwritten document image patches and then classify writer by Softmax loss function. The experiments are done on IAM English language dataset and obtained accuracy of 92.7%.
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References
Sreeraj, M., Idicula, S.M.: A survey on writer identification schemes. Int. J. Comput. Appl. 25(12), 23–33 (2011)
Bulacu, M., Schomaker, L.: Text-independent writer identification and verification using textural and allographic features. IEEE Trans. Pattern Analysis Mach. Intell. 29(4), 701–717 (2007)
Jain, R., Doermann, D.: Offline writer identification using K-adjacent segments. In: 2011 International Conference on Document Analysis and Recognition, Beijing, 2011, pp. 769–773
Wu, X., Tang, Y., Bu, W.: Offline text-independent writer identification based on scale invariant feature transform. IEEE Trans. Inf. Forensics Secur. 9(3), 526–536
Jain, R., Doermann, D.: Combining local features for offline writer identification. In: 2014 14th International Conference on Frontiers in Handwriting Recognition, Heraklion, 2014, pp. 583–588
Xing, L., Qiao, Y.: Deepwriter: a multi-stream deep CNN for text-independent writer identification. In: 2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR), Shenzhen, Oct 2016, pp. 584–589
Christlein, V., Gropp, M., Fiel, S., Maier, A.K.: Unsupervised feature learning for writer identification and writer retrieval. In International Conference on Document Analysis and Recognition (ICDAR), vol. 1, pp. 991–997, 2017
Chen, S., Wang, Y., Lin, C.-T., Ding, W., Cao, Z.: Semi-supervised feature learning for improving writer identification (2018)
Krizhevsky, A., Sutskever, I., Hinton, G. E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing (2012)
He, S., Schomaker, L.: FragNet: writer identification using deep fragment networks. IEEE Trans. Inf. Forensics Secur. 15, 3013–3022 (2020)
He, S., Schomaker, L.: Deep adaptive learning for writer identification based on single handwritten word images. Pattern Recogn. 88, 64 – 74 (2019)
Fiel, S., Sablatnig, R.: Writer identification and retrieval using a convolutional neural network. In: Computer Analysis of Images and Patterns, pp. 26–37. Springer
Rehman, Naz, S., Razzak, M.I., Ibrahim, H.A.: Automatic visual features for writer identification: a deep learning approach. IEEE Access 7, 17149–17157 (2019)
Keglevic, M., Fiel, S., Sablatnig, R.: Learning Features for Writer Retrieval and Identification using Triplet CNNs. ICFHR, pp. 211–216, 2018
Adak, C., Chaudhuri, B.B., Blumenstein, M.: Impact of struck-out text on writer identification. In: Proceedings of 30th International Joint Conference on Neural Networks (IJCNN), pp. 1465–1471, 2017
Nguyen, H.T., Nguyen, C.T., Ino, T., Indurkhya, B., Nakagawa, M.: Text-independent writer identification using convolutional neural network. Pattern Recogn. Lett. 121, 104–112 (2019)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556, 2014
He, K., Zhang, X., Ren, S., et al.: Deep residual learning for image recognition. arXiv:1512.03385, 2015
Ciresan, D., Meier, U., Schmidhuber, J.: Multi-column deep neural networks for image classification. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3642–3649. IEEE (2012)
Christlein, Maier, A.: Encoding CNN activations for writer recognition. In: Proceedings of 13th IAPR International Workshop Document Analysis System (DAS), pp. 169–174, Apr 2018
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Purohit, N., Panwar, S. (2022). Dual-Pathway Deep CNN for Offline Writer Identification. In: Troiano, L., et al. Advances in Deep Learning, Artificial Intelligence and Robotics. Lecture Notes in Networks and Systems, vol 249. Springer, Cham. https://doi.org/10.1007/978-3-030-85365-5_12
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DOI: https://doi.org/10.1007/978-3-030-85365-5_12
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