Papers › Disentangling Writer and Character Styles for Handwriting Generation

Disentangling Writer and Character Styles for Handwriting Generation

26 Mar 2023CVPR 2023 1arXiv:2303.14736archive 2025-07-28

Gang Dai, Yifan Zhang, Qingfeng Wang, Qing Du, Zhuliang Yu, Zhuoman Liu, Shuangping Huang

Training machines to synthesize diverse handwritings is an intriguing task. Recently, RNN-based methods have been proposed to generate stylized online Chinese characters. However, these methods mainly focus on capturing a person's overall writing style, neglecting subtle style inconsistencies between characters written by the same person. For example, while a person's handwriting typically exhibits general uniformity (e.g., glyph slant and aspect ratios), there are still small style variations in finer details (e.g., stroke length and curvature) of characters. In light of this, we propose to disentangle the style representations at both writer and character levels from individual handwritings to synthesize realistic stylized online handwritten characters. Specifically, we present the style-disentangled Transformer (SDT), which employs two complementary contrastive objectives to extract the style commonalities of reference samples and capture the detailed style patterns of each sample, respectively. Extensive experiments on various language scripts demonstrate the effectiveness of SDT. Notably, our empirical findings reveal that the two learned style representations provide information at different frequency magnitudes, underscoring the importance of separate style extraction. Our source code is public at: https://github.com/dailenson/SDT.

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assert_and_infer_cfg dailenson/SDT/parse_config.py official repository unverified MIT (permissive) · 87f89dec0143219f · report
build_transformer dailenson/SDT/models/transformer.py official repository unverified MIT (permissive) · d3c210804250a1dc · report
get_mixture_coef dailenson/SDT/models/gmm.py official repository unverified MIT (permissive) · 3938029b16a8e652 · report
get_pen_loss dailenson/SDT/models/loss.py official repository unverified MIT (permissive) · c3c5ed7bdf6dff50 · report
get_seq_from_gmm dailenson/SDT/models/gmm.py official repository unverified MIT (permissive) · 0a53368641da7207 · report
length_to_mask dailenson/SDT/models/eval_model.py official repository unverified MIT (permissive) · 61789a642c162a1b · report
mask_avr_pooling dailenson/SDT/models/eval_model.py official repository unverified MIT (permissive) · 977232b5fb66d03c · report
mask_avr_pooling_rnn dailenson/SDT/models/eval_model.py official repository unverified MIT (permissive) · 8acffa1b60bdf3a3 · report
set_log dailenson/SDT/utils/logger.py official repository unverified MIT (permissive) · 9f7efffab3bafdef · report
tf_2d_normal dailenson/SDT/models/loss.py official repository unverified MIT (permissive) · 67b19b6b4b262e2b · report
affine_transformation koninik/htg_evaluation/utils/auxilary_functions.py community (archive-listed) unverified MIT (permissive) · 22a46289db28df6d · report
centered koninik/htg_evaluation/utils/auxilary_functions.py community (archive-listed) unverified MIT (permissive) · ae6d12745b44744a · report
eval_class_epoch koninik/htg_evaluation/htg_style.py community (archive-listed) unverified MIT (permissive) · 271b9e1e885936bf · report
image_resize koninik/htg_evaluation/utils/auxilary_functions.py community (archive-listed) unverified MIT (permissive) · e8bf18bac8135a3c · report
performance koninik/htg_evaluation/htg_style.py community (archive-listed) unverified MIT (permissive) · 67e4aa07555e7628 · report
train_class_epoch koninik/htg_evaluation/htg_style.py community (archive-listed) unverified MIT (permissive) · f0595038a8dda9b6 · report

Tasks

Handwriting generation

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Methods

Absolute Position EncodingsAdamAttentionBPEDense ConnectionsDropoutLabel SmoothingLayer NormalizationLinear LayerMulti-Head AttentionPosition-Wise Feed-Forward LayerResidual ConnectionSoftmaxTransformer

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