Computer Science ›› 2019, Vol. 46 ›› Issue (11A): 264-267.

• Pattern Recognition & Image Processing • Previous Articles     Next Articles

Handwritten Drawing Order Recovery Method Based on Endpoint Sequential Prediction

ZHANG Rui1, ZHAN Yong-song2, YANG Ming-hao3   

  1. (Guangxi Experiment Center of Information Science,Guilin University of Electronic Technology,Guilin,Guangxi 541004,China)1;
    (Guangxi Key Laboratory of Trusted Software,Guilin University of Electronic Technology,Guilin,Guangxi 541004,China)2;
    (The National Laboratory of Pattern Recognition,Institute of Automation,Chinese Academy of Sciences,Beijing 100190,China)3
  • Online:2019-11-10 Published:2019-11-20

Abstract: To address the problem of dynamic sequential recovery for Chinese handwritten,a handwritten drawing order recovery model based on deep learning method was designed.First,the handwritten image is preprocessed by coordinate regularization,refinement,and interruption of intersections,then the preprocessed image and the corresponding written coordinate sequence are used to generate the sample of the network.The sample consists of a static handwritten image and a heat map label containing the font writing order.The model uses an end-to-end convolutional neural work.Finally,the trained network model is used to predict the static handwritten image to get the original writing order of the font.The experimental results show that the method can effectively recovery the drawing order of handwritten fonts that less than five strokes.

Key words: Handwriting, Time series information, Deep learning, Order recovery, Convolutional neural networks

CLC Number: 

  • TP183
[1]金连文,钟卓耀,杨钊,等.深度学习在手写汉字识别中的应用综述[J].自动化学报,2016,42(8):1125-1141.
[2]CORDELLA L P,STEFANO C D,MARCELLI A,et al.Writing Order Recovery from Off-Line Handwriting by Graph Traversal[J].IEEE International Conference on Pattern Recognition 2010:1896-1899.
[3]DINH M,YANG H J,LEE G S,et al.Recovery of drawing order from multi-stroke English handwritten images based on graph models and ambiguous zone analysis[J].Expert Systems with Applications,2016(64):352-364.
[4]LEMAIGNAN S,JACQ A,Hood D,et al.Learning by Teaching a Robot:The Case of Handwriting[J].IEEE Robotics & Automation Magazine,2016,23(2):56-66.
[5]YANG M H,ZHANG K,ZHAO BQ,et al.A Robotic Writing System with Intelligent Interactive Learning Ability[C]∥CHCI2017,2017.
[6]QIAO Y,NISHIARA M,YASUHARA M.A framework to-ward restoration of writing order from single-stroked handwriting image[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2006(28):1724-1737.
[7]BOCCIGNONE G,CHIANESE A,CORDELLA L P,et al.Recovering dynamic information from static handwriting[J].Pattern Recognition,1993,26(3):409-418.
[8]QIAO Y,YASUHARA M.Recovering Drawing Order from Offline Handwritten Image Using Direction Context and Optimal Euler Path[C]∥IEEE International Conference on Acoustics,Speech and Signal Processing.2006:II-II.
[9]NAGOYA T,FUJIOKA H.Recovering Human-Like Drawing Order from Static Handwritten Images with Double-Traced Lines[J].Lecture Notes in Electrical Engineering,2013(253):941-948.
[10]NGUYEN V,BLUMENSTEIN M.Techniques for static handwriting trajectory recovery:a survey[J].International Workshop on Document Analysis Systems,2010:463-470.
[11]曹忠升,苏哲文,王元珍.一种脱机手写汉字书写顺序恢复模型[J].中国图象图形学报,2009,14(10):2074-2081.
[12]SHARMA A.Recovery of drawing order in handwritten digit images[C]∥IEEE Second International Conference on Image Information Processing.IEEE,2014:437-441.
[13]NAKAI M,SHIMODAIRA H,SAGAYAMA S.Generation of hierarchical dictionary for stroke-order free Kanji handwriting recognition based on substroke HMM[C]∥International Conference on Document Analysis and Recognition.IEEE,2003(1):514-518.
[14]ZHANG X Y,YIN F,ZHANG Y M,et al.Drawing and Recognizing Chinese Characters with Recurrent Neural Network[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2018(99):1-1.
[15]BHUNIA A K,BHOWMICK A,BHUNIA A K,et al.Handwriting Trajectory Recovery using End-to-End Deep Encoder-Decoder Network[C]∥2018 24th International Conference on Pattern Recognition (ICPR).Beijing,China:IEEE,2018.
[16]LECUN Y L,BOTTOU L,BENGIO Y,et al.Gradient- Based Learning Applied to Document Recognition[J].Proceedings of the IEEE,1998,86(11):2278-2324.
[17]HE K,ZHANG X,REN S,et al.Deep residual learning for image recognition[C]∥Proceedings of the IEEE conference on Computer Vision and Pattern Recognition.2016:770-778.
[18]LECUN Y,BOSER B E,DENKER J S,et al.Handwritten digit recognition with a back-propagation network[C]∥Advances in Neural Information Processing Systems.SanFrancisco,CA,USA,1990:396-404.
[1] WANG Rui-ping, JIA Zhen, LIU Chang, CHEN Ze-wei, LI Tian-rui. Deep Interest Factorization Machine Network Based on DeepFM [J]. Computer Science, 2021, 48(1): 226-232.
[2] YU Wen-jia, DING Shi-fei. Conditional Generative Adversarial Network Based on Self-attention Mechanism [J]. Computer Science, 2021, 48(1): 241-246.
[3] TONG Xin, WANG Bin-jun, WANG Run-zheng, PAN Xiao-qin. Survey on Adversarial Sample of Deep Learning Towards Natural Language Processing [J]. Computer Science, 2021, 48(1): 258-267.
[4] DING Yu, WEI Hao, PAN Zhi-song, LIU Xin. Survey of Network Representation Learning [J]. Computer Science, 2020, 47(9): 52-59.
[5] HE Xin, XU Juan, JIN Ying-ying. Action-related Network:Towards Modeling Complete Changeable Action [J]. Computer Science, 2020, 47(9): 123-128.
[6] YE Ya-nan, CHI Jing, YU Zhi-ping, ZHAN Yu-liand ZHANG Cai-ming. Expression Animation Synthesis Based on Improved CycleGan Model and Region Segmentation [J]. Computer Science, 2020, 47(9): 142-149.
[7] DENG Liang, XU Geng-lin, LI Meng-jie, CHEN Zhang-jin. Fast Face Recognition Based on Deep Learning and Multiple Hash Similarity Weighting [J]. Computer Science, 2020, 47(9): 163-168.
[8] BAO Yu-xuan, LU Tian-liang, DU Yan-hui. Overview of Deepfake Video Detection Technology [J]. Computer Science, 2020, 47(9): 283-292.
[9] SUN Yan-li, YE Jiong-yao. Convolutional Neural Networks Compression Based on Pruning and Quantization [J]. Computer Science, 2020, 47(8): 261-266.
[10] YUAN Ye, HE Xiao-ge, ZHU Ding-kun, WANG Fu-lee, XIE Hao-ran, WANG Jun, WEI Ming-qiang, GUO Yan-wen. Survey of Visual Image Saliency Detection [J]. Computer Science, 2020, 47(7): 84-91.
[11] WANG Wen-dao, WANG Run-ze, WEI Xin-lei, QI Yun-liang, MA Yi-de. Automatic Recognition of ECG Based on Stacked Bidirectional LSTM [J]. Computer Science, 2020, 47(7): 118-124.
[12] LIU Yan, WEN Jing. Complex Scene Text Detection Based on Attention Mechanism [J]. Computer Science, 2020, 47(7): 135-140.
[13] ZHANG Zhi-yang, ZHANG Feng-li, TAN Qi, WANG Rui-jin. Review of Information Cascade Prediction Methods Based on Deep Learning [J]. Computer Science, 2020, 47(7): 141-153.
[14] JIANG Wen-bin, FU Zhi, PENG Jing, ZHU Jian. 4Bit-based Gradient Compression Method for Distributed Deep Learning System [J]. Computer Science, 2020, 47(7): 220-226.
[15] CHEN Jin-yin, ZHANG Dun-Jie, LIN Xiang, XU Xiao-dong and ZHU Zi-ling. False Message Propagation Suppression Based on Influence Maximization [J]. Computer Science, 2020, 47(6A): 17-23.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] LEI Li-hui and WANG Jing. Parallelization of LTL Model Checking Based on Possibility Measure[J]. Computer Science, 2018, 45(4): 71 -75 .
[2] SUN Qi, JIN Yan, HE Kun and XU Ling-xuan. Hybrid Evolutionary Algorithm for Solving Mixed Capacitated General Routing Problem[J]. Computer Science, 2018, 45(4): 76 -82 .
[3] ZHANG Jia-nan and XIAO Ming-yu. Approximation Algorithm for Weighted Mixed Domination Problem[J]. Computer Science, 2018, 45(4): 83 -88 .
[4] WU Jian-hui, HUANG Zhong-xiang, LI Wu, WU Jian-hui, PENG Xin and ZHANG Sheng. Robustness Optimization of Sequence Decision in Urban Road Construction[J]. Computer Science, 2018, 45(4): 89 -93 .
[5] SHI Wen-jun, WU Ji-gang and LUO Yu-chun. Fast and Efficient Scheduling Algorithms for Mobile Cloud Offloading[J]. Computer Science, 2018, 45(4): 94 -99 .
[6] ZHOU Yan-ping and YE Qiao-lin. L1-norm Distance Based Least Squares Twin Support Vector Machine[J]. Computer Science, 2018, 45(4): 100 -105 .
[7] LIU Bo-yi, TANG Xiang-yan and CHENG Jie-ren. Recognition Method for Corn Borer Based on Templates Matching in Muliple Growth Periods[J]. Computer Science, 2018, 45(4): 106 -111 .
[8] GENG Hai-jun, SHI Xin-gang, WANG Zhi-liang, YIN Xia and YIN Shao-ping. Energy-efficient Intra-domain Routing Algorithm Based on Directed Acyclic Graph[J]. Computer Science, 2018, 45(4): 112 -116 .
[9] CUI Qiong, LI Jian-hua, WANG Hong and NAN Ming-li. Resilience Analysis Model of Networked Command Information System Based on Node Repairability[J]. Computer Science, 2018, 45(4): 117 -121 .
[10] WANG Zhen-chao, HOU Huan-huan and LIAN Rui. Path Optimization Scheme for Restraining Degree of Disorder in CMT[J]. Computer Science, 2018, 45(4): 122 -125 .