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CircleID: ICDAR 2026 Competition on Writer and Pen Identification from Hand-Drawn Circles

Thomas Gorges1 Janne van der Loop2 Fei Wu1 Mathias Seuret1 Vincent Christlein1
1 Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany 2 Buchwissenschaft, Johannes Gutenberg-Universität Mainz, Germany
Dataset Baseline Kaggle: Writer Task Kaggle: Pen Task How to submit Contact

Description

CircleID is the ICDAR 2026 competition on identifying who drew a circle and which pen was used, using only scanned images of hand-drawn circles. Although a circle is a simple shape, it contains rich, subtle cues from both human motor behavior and pen/ink characteristics. The challenge is to learn representations that disentangle writer style from pen properties in static images.

Participants receive a new dataset of 40250 scanned, circle images collected under controlled conditions from 51 writers and 8 pens. Two tasks are evaluated: (1) writer identification (with an “unknown writer” class) and (2) pen classification.

Samples

Randomly selected circle crops from the training set.

Mosaic of circle samples

Tasks

CircleID features two related classification tasks on static images of hand-drawn circles. The goal is to learn visual representations that separate writer-specific traits from pen-specific traits (ink deposition, stroke texture, width).

Task 1: Writer Identification

  • Objective: Predict the writer ID for circles drawn by known writers. For circles drawn by writers not present in training, output -1 (unknown writer).
  • Out-of-distribution: The test set includes circles from unseen writers (not present in training). For these samples, participants must output -1 as the predicted writer ID.

Task 2: Pen Classification

  • Objective: Predict which of the predefined pen types was used to draw each circle. This task tests robustness across both known and unseen writers.
  • Applies to all test images: A pen prediction is required for every sample, including those from unseen writers.

Key challenges include feature entanglement (writer habits vs. pen properties) and high intra-writer variability despite strong inter-writer similarity.

Dataset

The CircleID dataset contains 40250 tightly-cropped images of hand-drawn circles collected in a controlled study with 51 writers using 8 different pens. Templates were digitized with high-resolution scans (400 dpi), and each circle was automatically extracted and manually linked to ground truth labels.

  • Images: PNG files, one circle per image.
  • Labels: Writer ID and pen ID (only provided for training set).

Data split

The dataset is split using a hybrid protocol to evaluate both writer identification and generalization for pen classification:

  • Known writers (44): Used for training and testing writer identification.
  • Unseen writers (7): Held out entirely for the final test set (Part A).
  • Training set: 70% of images from the 44 known writers.
  • Test set Part B: Remaining 30% of images from the 44 known writers.
  • Final test set: Part A (all images from unseen writers) + Part B.

The dataset can be accessed through the Kaggle competitions pages (Writer Identification & Pen Classification). Ground truth for the test set remains confidential during the competition.

Training data

  • Images: Directory of .png files.
  • Manifest: train.csv with columns: image_id, image_path, writer_id, pen_id.

Test data

  • Images: Directory of .png files.
  • Manifest: test.csv with columns: image_id, image_path.
  • Ground truth: Not provided for the test set.

Submission Format

Your submission

CircleID is hosted as two separate Kaggle competitions with independent leaderboards and submission files: one for writer identification and one for pen classification.

  • Writer Identification competition: Submit submission_writer.csv with columns image_id, writer_id. For unseen writers, set writer_id to -1.
  • Pen Classification competition: Submit submission_pen.csv with columns image_id, pen_id. A pen prediction is required for all test images.
Kaggle competition pages:

Evaluation

CircleID includes two independent leaderboards, one per task. Rankings are based on top-1 accuracy.

Task 1 Leaderboard: Writer Identification

  • Metric: Top-1 accuracy.
  • Evaluation data: Full test set (Part A + Part B). For Part B, predict the correct known writer ID. For Part A, predict -1.

Task 2 Leaderboard: Pen Classification

  • Primary metric: Top-1 accuracy.
  • Supplementary metric: Macro-averaged F1-score (reported for analysis).
  • Evaluation data: Full test set (Part A + Part B) to measure robustness across both known and unseen writers.

Leaderboard protocol: During the competition, the leaderboard is computed using only 30% of the test set annotations (public leaderboard). The remaining 70% of the test set annotations are held back (private leaderboard) and are used to update the final rankings at the end of the challenge.

Winners will be announced for each leaderboard at the conclusion of the competition.

Baseline

We provide a simple but effective baseline to help participants get started and to establish an initial benchmark. The baseline is a ResNet model pre-trained on ImageNet and fine-tuned on CircleID. A single training script supports both tasks via a TASK switch.

  • Backbone: ResNet feature extractor pre-trained on ImageNet.
  • Head: One task-specific fully connected classification layer.
  • Two modes: Set TASK="writer" for writer ID prediction, or TASK="pen" for pen ID prediction.
  • Code: Training and inference code are located in the Kaggle Challenges (Writer Identification & Pen Classification) and on GitHub.

Timeline

  • Feb 3, 2026: Registration and submission to Kaggle open; Training data with ground truth released; Hold-out test set without ground truth released
  • April 3, 2026: Official submission deadline.
  • April 17, 2026: Submission of the initial competition report.
  • May 4, 2026: Camera-ready report submission.
  • June 22, 2026: Winners communicated to the chairs.

Note: Submissions remain open after the official end of the competition to support ongoing benchmarking.

Contact

For questions about the CircleID competition (dataset access, rules, submissions, evaluation), please contact the organizers: