臺灣博碩士論文加值系統

書法藝術作為中華地區最重要的文化之一，在數千年的發展背景之下，其中演變出了許多不同的字體，每一種都附含著重要的歷史意義和藝術價值，而隨著國際化的趨勢，各國之間的交流帶動了文化傳播，使得書法藝術在世界各地都有不少的愛好者。然而，書法鑑賞需要長時間的培養，一般民眾不容易區分出字型間的差異，在非中文語系的國家，更是難以了解書法文化，與此同時，有不少人企圖利用偽造的作品，騙取他人購買贗品。近年來對於影像的真偽辨識一直是受到關注的議題，因此需要建立一個書法辨識系統來區分作品的真偽性以解決仿冒問題。本研究主要透過讓機器學習不同書法家之間的個人書寫特色，從細微的文字特徵中觀察出不屬於真實作品的差異，以此來辨識出仿冒的書法作品。

本研究將從收集到的書法文字中，分別使用Gram Matrix提取出文字的紋理特徵，以及Gabor Filter提取出筆畫特徵，透過資料融合的方式結合兩種影像特徵來建立卷積神經網路訓練模型學習辨識書法字型。在真偽性辨識的實驗中，研究尋找兩位書法家擔任受試者，盡可能的寫出與目標書法作品相同風格的文字，以此作為仿冒品來進行真偽性辨識。本研究所選擇的書法字體為楷書，作為字行變化與現今使用的中文最相近的字體，同時楷書對於一般民眾相對容易學習，因此以楷書進行實驗分析。

As one of the most important cultures in China, calligraphy was evolved many different styles from thousands of years history. For globalization, calligraphy is not only famous in China but also in lots of contries. However, it needs a long-time training to appreciate calligraphy. It is not easy for the general public to distinguish the differences between calligraphy styles specially in non-Chinese speaking countries, it is even more difficult to understand calligraphy culture. At the same time, some people try to use forged artworks to defraud others’ money. In recent years, the authenticity recognition of images has always been a hot topic. Therefore, it is necessary to develope a identification system to distinguish the authenticity of calligraphy to solve the problem. This research analyzes the personal writing features of different calligraphers and observes the differences between words to determind which is not real works.
This research using Gram Matrix to extract the texture features and using Gabor Filter to extract the stroke features of the words from the collected calligraphy texts, and combine the two image features through data fusion to build a convolutional neural network to identify calligraphy styles. In the experiment of authenticity identification, the researcher asked two calligraphers as subjects, and wrote words as similar as possible as the target calligraphy artworks, using them as forged artworks for authenticity identification. The calligraphy style selected in this research is Regular script because it is the most similar to the currently Chinese words. At the same time, Regular script is easy to learn for the general public, so Regular script is used for experimental analysis.

摘 要 i
ABSTRACT ii
誌 謝 iii
目 錄 iii
表 目 錄 v
圖 目 錄 vi
第一章 緒論 1
1.1 前言 1
1.2 研究背景 1
1.3 研究動機 2
1.4 定義問題領域 4
1.5 研究步驟 4
1.6 論文組織架構 5
第二章 文獻與相關技術之探討 6
2.1 影像辨識相關應用研究 6
2.1.1 影像文字辨識相關應用 7
2.1.2 影像於真偽辨識相關應用 8
2.2 書法與漢字字形分析相關應用研究 8
2.3 影像風格相關應用研究 9
2.3.1 文字風格相關應用 10
2.4 資料融合技術相關應用研究 11
2.4.1 資料融合於影像識別相關應用 11
2.4.2 資料融合方法相關應用 12
2.5 機器學習相關技術介紹 13
2.6 卷積神經網路介紹 14
2.6.1 激活函數介紹 17
2.6.2 損失函數介紹 18
2.6.3 損失函數介紹 19
2.7 CNN衍生模型介紹 20
2.7.1 AlexNet 21
2.7.2 VGGNet 22
2.7.3 GoogLeNet 23
2.7.4 ResNet 24
2.8 資料融合技術介紹 25
2.8.1 信號級融合 26
2.8.2 特徵級融合 27
2.8.3 決策級融合 27
2.9 Gram Matrix介紹 29
2.10 Gabor Filter介紹 30
2.11 書法字體簡介 31
2.11.1 篆書 33
2.11.2 隸書 34
2.11.3 草書 35
2.11.4 行書 36
2.11.5 楷書 37
第三章 實驗設計與建置 38
3.1 本研究之系統架構 38
3.2 資料處理與蒐集方式 39
3.2.1 楷書字型選擇 40
3.2.2 文字提取與訓練資料建立 41
3.3 實驗設計 43
3.4 實驗工具 46
第四章 實驗結果與討論 47
4.1 實驗結果 47
4.2 討論與分析 51
4.3 Baseline模型效能比較 52
第五章 系統效能評估 53
5.1 案例一：柳公權楷書評估 54
5.2 案例二：沈尹默楷書評估 60
第六章 結論與未來展望 66
6.1 研究結論與探討 66
6.2 未來工作展望 67
參考文獻 68

[1]Gao, P., Gu, G., Wu, J., and Wei, B., “Chinese calligraphic style representation for recognition”, In Journal of International Journal on Document Analysis and Recognition (IJDAR), Vol. 20, pp. 59-68, January 2017.

[2]Liang, J., Liao, W. H., and Wu, Y. C., “Toward Automatic Recognition of Cursive Chinese Calligraphy”, In Proceeding of International Conference on Ubiquitous Information Management and Communication (IMCOM), February 2020.

[3]Wang, Z., Yan, W., Guo, M., and Zhang, J., “Exploiting Gabor Feature Extraction Method for Chinese Character Writing Quality Evaluation”, In Journal of Physics Conference Series, June 2020.

[4]Wang, K., Yi, Y., Liu, J., Lu, L., and Song, Y., “Multi-scene ancient chinese text recognition”, In Journal of Neurocomputing, Vol. 377, pp. 64-72, February 2020.

[5]Wen, Y., and Sigüenza, J. A., “Chinese Calligraphy: Character Style Recognition based on Full-page Document”, In Proceeding of the 2019 8th International Conference on Computing and Pattern Recognition, pp. 390-394, October 2019.

[6]Gatys, L. A., Ecker, A. S., and Bethge, M., “A Neural Algorithm of Artistic Style”, In Journal of Vision, Vol. 16, September 2016.

[7]Karayev, S., Trentacoste, M., Han, H., Agarwala, A., Darrell, T., Hertzmann, A., and Winnemoeller, H., “Recognizing Image Style”, In Proceeding of the British Machine Vision Conference 2014, July 2014.

[8]Zhu, J. Y., Park, T., Isola, P., and Efros, A. A., “Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”, In Proceeding of the IEEE International Conference on Computer Vision (ICCV), pp. 2223-2232, 2017.
[9]Sun, T., Wang, Y., Yang, J., and Hu, X., “Convolution neural networks with two pathways for image style recognition”, In Processing of IEEE Transactions on Image Processing, Vol. 26, pp. 4102-4113, 2017.

[10]Gao, Y., Guo, Y., Lian, Z., Tang, Y., and Xiao, J., “Artistic glyph image synthesis via one-stage few-shot learning”, In Journal of ACM Transactions on Graphics (TOG), Vol. 38, pp. 1-12, 2019.

[11]Gao, Y., and Wu, J., “GAN-Based Unpaired Chinese Character Image Translation via Skeleton Transformation and Stroke Rendering”, In Proceeding of the AAAI Conference on Artificial Intelligence, Vol. 34, April 2020.

[12]Castanedo, F., “A Review of Data Fusion Techniques”, In Journal of The Scientific World Journal 2013, October 2013.

[13]Akbar, A., Kousiouris, G., Pervaiz, H., Sancho, J., Ta-Shma, P., Carrez, F., and Moessner, K., “Real-Time Probabilistic Data Fusion for Large-Scale IoT Applications”, In Journal of IEEE Access, Vol. 6, pp. 10015-10027, February 2018.

[14]Bachmann, C., Abdulhai, B., Roorda, M. J., and Moshiri, B., “A comparative assessment of multi-sensor data fusion techniques for freeway traffic speed estimation using microsimulation modeling”, In Journal of Transportation Research Part C: Emerging Technologies, Vol. 26, pp. 33-48, January 2013.

[15]Audebert, N., Herold, C., Slimani, K., and Vidal, C., “Multimodal deep networks for text and image-based document classification”, In Proceeding of Machine Learning and Knowledge Discovery in Databases, pp. 427-443, March 2020.

[16]Li, C., Sanchez, R. V., Zurita, G., Cerrada, M., Cabrera, D., and Vásquez, R. E., “Multimodal deep support vector classification with homologous features and its application to gearbox fault diagnosis”, In Journal of Neurocomputing, Vol. 168, pp. 119-127, November 2015.

[17]Zhang, Y., Huang, Q., Ma, X., Yang, Z., and Jiang, J., “Using Multi-features and Ensemble Learning Method for Imbalanced Malware Classification”, In Proceeding of IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), February 2017.

[18]Guo, Z., Li, X., Huang, H., Guo, N., and Li, Q., “Deep Learning-Based Image Segmentation on Multimodal Medical Imaging”, In Journal of IEEE Transactions on Radiation and Plasma Medical Sciences, Vol. 3, pp. 162-169, January 2019.

[19]Caesar, H., Bankiti, V., Lang, A. H., Vora, S., Liong, V. E., Xu, Q., Krishnan, A., Pan, Y., Baldan, G., and Beijbom, O., “nuScenes: A multimodal dataset for autonomous driving”, In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 11621-11631, 2020.

[20]Sun, Y., Li, D., Wu, X., and Hao, Q., “Visual Perception Based Situation Analysis of Traffic Scenes for Autonomous Driving Applications”, In Proceedings of International Conference on Intelligent Transportation, December 2020.

[21]Ren, Y., Yang, J., Zhang, Q., and Guo, Z., “Multi-Feature Fusion with Convolutional Neural Network for Ship Classification in Optical Images”, In Journal of Computing and Artificial Intelligence, October 2019.

[22]Zhang, H., Qu, S., Li, H., Luo, J., and Xu, W., “A Moving Shadow Elimination Method Based on Fusion of Multi-Feature”, In Journal of IEEE Access, Vol. 8, March 2020.

[23]Boström, H., Andler, S. F., Brohede, M., and Johansson, R., “On the definition of information fusion as a field of research”, January 2007.

[24]Khaleghi, B., Khamis, A., Karray, F. O., and Razavi, S. N., “Multisensor data fusion: A review of the state-of-the-art”, In Journal of Information Fusion, Vol. 14, pp. 28-44, January 2013.

[25]Atrey, P. K., Hossain, M. A., Saddik, A. E., and Kankanhalli, M. S., “Multimodal fusion for multimedia analysis: a survey”, In Journal of Multimedia Systems, Vol. 16, pp. 345-379, April 2010.

[26]Poria, S., Cambria, E., Bajpai, R., and Hussain, A., “A review of affective computing: From unimodal analysis to multimodal fusion”, In Journal of Information Fusion, Vol. 37, pp. 98-125, September 2017.

[27]Meng, T., Jing, X., Yan, Z., and Pedrycz, W., “A survey on machine learning for data fusion”, In Journal of Information Fusion, Vol. 57, pp. 115-129, May 2020.

[28]White, F. E., “DATA FUSION LEXICON”, 1991.

[29]Hall, D. L., and Llinas, J., “An Introduction to Multisensor Data Fusion”, In Proceedings of the IEEE, Vol. 85, pp. 6-23, 1997.

[30]Luo, R. C., and Kay, M. G., “Multisensor integration and fusion: issues and approaches”, In Proceedings of Sensor Fusion, Vol. 0931, August 1988.

[31]Gadzicki, K., Khamsehashari, R., and Zetzsche, C., “Early vs Late Fusion in Multimodal Convolutional Neural Networks”, In Proceedings of International Conference on Information Fusion, September 2020.

[32]Wang, H. A., and Liu, P., T., “Towards a Higher Accuracy of Optical Character Recognition of Chinese Rare Books in Making Use of Text Model”, In Proceedings of the 3rd International Conference on Digital Access to Textual Cultural Heritage, pp. 15-18, May 2019.
[33]Shi, B., Bai, X., and Yao, C., “An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition”, In Journal of IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 39, pp. 2298-2304, December 2016.

[34]Sonavane, K., Soni, B., and Majhi U., “Survey on Automatic Number Plate Recognition (ANR)”, In International Journal of Computer Applications, pp. 1-4, September 2015.

[35]Liu, J., Zheng, F., van Zuylen, H. J., and Li, J., “A dynamic OD prediction approach for urban networks based on automatic number plate recognition data”, In Journal of Transportation Research Procedia, Vol. 47, pp. 601-608, 2020.

[36]Zaklouta, F., and Stanciulescu, B., “Real-time traffic sign recognition in three stages”, In Journal of Robotics and Autonomous Systems, Vol. 62, pp. 16-24, January 2014.

[37]LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P., “Gradient-Based Learning Applied to Document Recognition”, In Proceedings of the IEEE, Vol. 86, pp. 2278-2324, 1998.

[38]Altwaijry, N., and Al-Turaiki, I., “Arabic handwriting recognition system using convolutional neural network”, In Journal of Neural Computing and Applications, Vol. 33, pp. 2249-2261, 2021.

[39]Singh, H., Sharma, R. K., and Singh, V. P., “Online handwriting recognition systems for Indic and non-Indic scripts: a review”, In Journal of Artificial Intelligence Review, Vol. 54, pp. 1525-1579, 2021.

[40]Sun, L., Su, T., Liu, C., and Wang, R., “Deep LSTM Networks for Online Chinese Handwriting Recognition”, In Proceeding of International Workshop on Frontiers in Handwriting Recognition, January 2017.

[41]Krizhevsky, A., Sutskever, I., and Hinton, G. E., “ImageNet Classification with Deep Convolutional Neural Networks”, In Proceeding of Advances in neural information processing systems, Vol. 25, pp. 1097-1105, 2012.

[42]Simonyan, K., and Zisserman, A. “Very Deep Convolutional Networks for Large-Scale Image Recognition”, In arXiv preprint arXiv:1409.1556, 2014.

[43]Liu, W., Wen, Y., Yu, Z., Li, M., Raj, B., and Song, L., “SphereFace: Deep Hypersphere Embedding for Face Recognition”, In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 212-220, 2017.

[44]Lecoutre, A., Negrevergne, B., and Yger, F., “Recognizing Art Style Automatically in Painting with Deep Learning”, In Proceedings of the Ninth Asian Conference on Machine Learning, pp. 327-342, 2017.

[45]Yang, H., and Min, K., “A Multi-Column Deep Framework for Recognizing Artistic Media”, In Journal of Electronics, Vol. 8, November 2019.

[46]Assayony, M. O., and Mahmoud, S. A., “Recognition of Arabic Handwritten Words using Gabor-based Bag-of-Features Framework”, In International Journal of Computing and Digital Systems, Vol. 7, pp. 35-42, 2018

[47]Glodek, M., Reuter, S., Schels, M., Dietmayer, K., and Schwenker, F., “Kalman Filter Based Classifier Fusion for Affective State Recognition”, In Proceeding of International workshop on multiple classifier systems, pp. 85-94, May 2013.

[48]Dobrišek, S., Gajšek, R., Mihelič, F., Pavešić, N., and Štruc, V., “Towards Efficient Multi-Modal Emotion Recognition”, In International Journal of Advanced Robotic Systems, January 2013.
[49]Dasarathy, B.V., “Sensor fusion potential exploitation-innovative architectures and illustrative applications”, In Proceedings of the IEEE, Vol. 85, pp. 24-38, January 1997.

[50]Fukushima, K., “Neocognitron: A Self-organizing Neural Network Model for a Mechanism of Pattern Recognition Unaffected by Shift in Position”, In Journal of Biological Cybernetics, Vol. 36, pp. 193-202, 1980.

[51]LeCun, Y., Bottou, L., Orr, G. B., and Müller, K. R., “Efficient BackProp”, In Neural Networks: Tricks of the Trade, pp. 9-48, 2012.

[52]Olshausen, B. A., and Field, D. J., “Emergence of simple-cell receptive field properties by learning a sparse code for natural Images”, In Journal of Nature, Vol. 13, pp.607-609, June 1996.

[53]Samuel, A. L., “Some studies in machine learning using the game of checkers”, In IBM Journal of Research and Development, Vol. 3, pp. 210-229, July 1959.

[54]Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich A., “Going Deeper with Convolutions.”, In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1-9, 2015.

[55]Ioffe, S., and Szegedy, C., “Batch Normalization:Accelerating Deep Network Training by Reducing Internal Covariate Shift.”, In Proceedin of International conference on machine learning, pp. 448-456, June 2015.

[56]Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z., “Rethinking the Inception Architecture for Computer Vision”, In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2818-2826, June 2016

[57]Szegedy, C., Ioffe, S., Vanhoucke, V., and Alemi, A. A., “Inception-v4,Inception-ResNet and the Impact of Residual Connections on Learning.”, In Proceedings of Thirty-first AAAI conference on artificial intelligence, February 2017.

[58]He, K., Zhang, X., Ren, S., and Sun, J., “Deep Residual Learning for Image Recognition”, In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770-778, 2016.

[59]Arora, S., Bhaskara, A., Ge, R., and Ma, T., “Provable Bounds for Learning Some Deep Representations”, In Proceeding of International conference on machine learning, pp. 584-592, January 2014.

[60]Polak, A., Kelman, T., Murray, P., Marshall, S., Stothard, D. J., Eastaugh, N., and Eastaugh, F., “Hyperspectral Imaging Combined With Data Classification Techniques as An Aid for Artwork Authentication.”, In Journal of Cultural Heritage, Vol. 26, pp. 1-11, July 2017.

[61]Cha, S. H., and Tappert, C. C., “Automatic Detection of Handwriting Forgery.” In Proceedings Eighth International Workshop on Frontiers in Handwriting Recognition, pp. 264-267, August 2002.

[62]Fahn, C. S., Lee, C. P., and Chen, H. I., “A Text Independent Handwriting Forgery Detection System Based on Branchlet Features and Gaussian Mixture Models.” In Proceedings of 2016 14th Annual Conference on Privacy, Security and Trust (PST), pp. 690-697, December 2016.

[63]Hafemann, L. G., Sabourin, R., and Oliveira, L. S., “Learning Features for Offline Handwritten Signature Verification Using Deep Convolutional Neural Networks.” In Journal of Pattern Recognition, Vol. 70, pp. 163-176, October 2017.