Methods › Computer Vision › Scene Text Models › ABCNet

Adaptive Bezier-Curve Network

ABCNet

3 papers tagged archive 2025-07-28

Introduced by Yuliang Liu et al. in ABCNet: Real-time Scene Text Spotting with Adaptive Bezier-Curve Network

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

Adaptive Bezier-Curve Network, or ABCNet, is an end-to-end framework for arbitrarily-shaped scene text spotting. It adaptively fits arbitrary-shaped text by a parameterized bezier curve. It also utilizes a feature alignment layer, BezierAlign, to calculate convolutional features of text instances in curved shapes. These features are then passed to a light-weight recognition head.

PaperSource

Papers archive 2025-07-28

3 shown of 3, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

4 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Text Spotting2
Document Layout Analysis1
Scene Text Detection1
Text Detection1

Usage over time archive 2025-07-28

Papers per year tagged with ABCNet: 2020 to 2021, peak 2 2 0 2020: 2 papers 2020 2021: 1 paper 2021
Papers per year the archive tags with this method, by the paper's archive date (3 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Scene Text Models

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections