Sketch Recognition

Benchmarks archive 2025-07-28

No benchmark for this task in the archive.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

4 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

1 subtask in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

12 shown of 12 papers with code (39 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Sketch-a-Net that Beats Humans
  30 Jan 2015 3 repositories listed Syntology ran 2 of 2 samples · 0 unverified
  We propose a multi-scale multi-channel deep neural network framework that, for the first time, yields sketch recognition performance surpassing that of humans.
• Flowmind2Digital: The First Comprehensive Flowmind Recognition and Conversion Approach
  8 Jan 2024 1 repository listed
  Automated conversion methods are essential to overcome manual conversion challenges.
• Enhance Sketch Recognition's Explainability via Semantic Component-Level Parsing
  13 Dec 2023 1 repository listed
  Humans can recognize varied sketches of a category easily by identifying the concurrence and layout of the intrinsic semantic components of the category, since humans draw free-hand sketches based a common consensus…
• SEVA: Leveraging sketches to evaluate alignment between human and machine visual abstraction
  5 Dec 2023 1 repository listed
  Sketching is a powerful tool for creating abstract images that are sparse but meaningful.
• Abstracting Sketches through Simple Primitives
  27 Jul 2022 1 repository listed Syntology ran 10 of 16 samples · 6 unverified · 16 pointer-only (licence)
  Toward equipping machines with such capabilities, we propose the Primitive-based Sketch Abstraction task where the goal is to represent sketches using a fixed set of drawing primitives under the influence of a budget.
• Edge Augmentation for Large-Scale Sketch Recognition without Sketches
  26 Feb 2022 1 repository listed
  To bridge the domain gap we present a novel augmentation technique that is tailored to the task of learning sketch recognition from a training set of natural images.
• Sketch-BERT: Learning Sketch Bidirectional Encoder Representation from Transformers by Self-supervised Learning of Sketch Gestalt
  19 May 2020 1 repository listed
  Unfortunately, the representation learned by SketchRNN is primarily for the generation tasks, rather than the other tasks of recognition and retrieval of sketches.
• Multi-Graph Transformer for Free-Hand Sketch Recognition
  24 Dec 2019 1 repository listed Syntology ran 0 of 4 samples · 4 unverified
  In this work, we propose a new representation of sketches as multiple sparsely connected graphs.
• Distribution-Aware Binarization of Neural Networks for Sketch Recognition
  9 Apr 2018 1 repository listed
  We present a theoretical analysis of the technique to show the effective representational power of the resulting layers, and explore the forms of data they model best.
• SketchMate: Deep Hashing for Million-Scale Human Sketch Retrieval
  4 Apr 2018 1 repository listed
  Key to our network design is the embedding of unique characteristics of human sketch, where (i) a two-branch CNN-RNN architecture is adapted to explore the temporal ordering of strokes, and (ii) a novel hashing loss is…
• Enabling My Robot To Play Pictionary : Recurrent Neural Networks For Sketch Recognition
  11 Aug 2016 1 repository listed
  In our work, we propose a recurrent neural network architecture for sketch object recognition which exploits the long-term sequential and structural regularities in stroke data in a scalable manner.
• From Paper to Machine: Extracting Strokes from Images for use in Sketch Recognition
  1 Jan 2008 1 repository listed
  Online sketches provide significantly more information than paper sketches, but they still do not provide the flexibility, naturalness, and simplicity of a simple piece of paper.

Syntology lines on 3 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.