RECORD
Stroke Extraction of Chinese Character Based on Deep Structure Deformable Image Registration
Computer Vision and Pattern Recognition · Submitted 10 Jul 2023
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim The experimental results show that our method strongly outperforms the baselines.
The compiled record
The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.
Open the compiled record
The abstract, sentence by sentence
Stroke extraction of Chinese characters plays an important role in the field of character recognition and generation. The most existing character stroke extraction methods focus on image morphological features. These methods usually lead to errors of cross strokes extraction and stroke matching due to rarely using stroke semantics and prior information. In this paper, we propose a deep learning-based character stroke extraction method that takes semantic features and prior information of strokes into consideration. This method consists of three parts: image registration-based stroke registration that establishes the rough registration of the reference strokes and the target as prior information; image semantic segmentation-based stroke segmentation that preliminarily separates target strokes into seven categories; and high-precision extraction of single strokes. In the stroke registration, we propose a structure deformable image registration network to achieve structure-deformable transformation while maintaining the stable morphology of single strokes for character images with complex structures. In order to verify the effectiveness of the method, we construct two datasets respectively for calligraphy characters and regular handwriting characters. The experimental results show that our method strongly outperforms the baselines. Code is available at https://github.com/MengLi-l1/StrokeExtraction.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Stroke extraction of Chinese characters plays an important role in the field of character recognition and generation. | background 1.00 | 0.05 | 0.02 | asserted 1.00 |
| s1 | The most existing character stroke extraction methods focus on image morphological features. | background 0.99 | 0.05 | 0.01 | asserted 1.00 |
| s2 | These methods usually lead to errors of cross strokes extraction and stroke matching due to rarely using stroke semantics and prior information. | gap 0.84 | 0.06 | 0.03 | asserted 0.91 |
| s3 | In this paper, we propose a deep learning-based character stroke extraction method that takes semantic features and prior information of strokes into consideration. | contribution 0.99 | 0.91 | 0.16 | asserted 0.98 |
| s4 | This method consists of three parts: image registration-based stroke registration that establishes the rough registration of the reference strokes and the target as prior information; image semantic segmentation-based stroke segmentation that preliminarily separates target strokes into seven categories; and high-precision extraction of single strokes. | method 0.98 | 0.87 | 0.05 | asserted 0.99 |
| s5 | In the stroke registration, we propose a structure deformable image registration network to achieve structure-deformable transformation while maintaining the stable morphology of single strokes for character images with complex structures. | method 0.96 | 0.91 | 0.05 | asserted 0.98 |
| s6 | In order to verify the effectiveness of the method, we construct two datasets respectively for calligraphy characters and regular handwriting characters. | method 0.89 | 0.95 | 0.02 | asserted 0.96 |
| s7 | The experimental results show that our method strongly outperforms the baselines. | result 0.99 | 0.95 | 0.81 | asserted 1.00 |
| s8 | Code is available at https://github.com/MengLi-l1/StrokeExtraction. | contribution 0.58 | 0.45 | 0.02 | asserted 0.89 |
What was examined, and what was not
- arXiv listing abstract v1
- full text
- figures and tables
- references and citing sentences
- relations to other papers
- P abstract
- S sections
- T body sentences
- C citations
- R cross-paper relations
The paper
Original abstract
Stroke extraction of Chinese characters plays an important role in the field of character recognition and generation. The most existing character stroke extraction methods focus on image morphological features. These methods usually lead to errors of cross strokes extraction and stroke matching due to rarely using stroke semantics and prior information. In this paper, we propose a deep learning-based character stroke extraction method that takes semantic features and prior information of strokes into consideration. This method consists of three parts: image registration-based stroke registration that establishes the rough registration of the reference strokes and the target as prior information; image semantic segmentation-based stroke segmentation that preliminarily separates target strokes into seven categories; and high-precision extraction of single strokes. In the stroke registration, we propose a structure deformable image registration network to achieve structure-deformable transformation while maintaining the stable morphology of single strokes for character images with complex structures. In order to verify the effectiveness of the method, we construct two datasets respectively for calligraphy characters and regular handwriting characters. The experimental results show that our method strongly outperforms the baselines. Code is available at https://github.com/MengLi-l1/StrokeExtraction.
Human review
How the literature uses it
What this paper does with the arXiv papers it cites
What it cites · 1 sentence without a confident label
Uses the method or tool of 1 paper
-
We use the network architecture adapted from the Deeplabv3 model [ref] as the main frame of the SegNet to segment strokes guided by prior information.
Rethinking Atrous Convolution for Semantic Image Segmentation · SegNet for Separating Strokes Roughly · 0.97
Discussion
Know something about this paper? Leave a comment, or highlight any passage above to comment on it.