Complex Handwriting Trajectory Recovery: Evaluation Metrics and Algorithm

Zhounan Chen, Daihui Yang, Jinglin Liang, Xinwu Liu, Yuyi Wang, Zhenghua Peng, Shuangping Huang; Proceedings of the Asian Conference on Computer Vision (ACCV), 2022, pp. 1060-1076


Many important tasks such as forensic signature verification, calligraphy synthesis, etc, rely on handwriting trajectory recovery of which, however, even an appropriate evaluation metric is still missing. Indeed, existing metrics only focus on the writing orders but overlook the fidelity of glyphs. Taking both facets into account, we come up with two new metrics, the adaptive intersection on union (AIoU) which eliminates the influence of various stroke widths, and the length-independent dynamic time warping (LDTW) which solves the trajectory-point alignment problem. After that, we then propose a novel handwriting trajectory recovery model named Parsing-and-tracing ENcoder-decoder Network (PEN-Net), in particular for characters with both complex glyph and long trajectory, which was believed very challenging. In the PEN-Net, a carefully designed double-stream parsing encoder parses the glyph structure, and a global tracing decoder overcomes the memory difficulty of long trajectory prediction. Our experiments demonstrate that the two new metrics AIoU and LDTW together can truly assess the quality of handwriting trajectory recovery and the proposed PEN-Net exhibits satisfactory performance in various complex-glyph languages including Chinese, Japanese and Indic. The source code is available at

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@InProceedings{Chen_2022_ACCV, author = {Chen, Zhounan and Yang, Daihui and Liang, Jinglin and Liu, Xinwu and Wang, Yuyi and Peng, Zhenghua and Huang, Shuangping}, title = {Complex Handwriting Trajectory Recovery: Evaluation Metrics and Algorithm}, booktitle = {Proceedings of the Asian Conference on Computer Vision (ACCV)}, month = {December}, year = {2022}, pages = {1060-1076} }