Pith. Sign in

RECORD

Enhancing 3D-Air Signature by Pen Tip Tail Trajectory Awareness: Dataset and Featuring by Novel Spatio-temporal CNN

PaperPDF from arXiv

data files

Paper breakdownThe authors' claims, conditions and measurements, as JSON Signed receiptProof this review has not been altered
X Bluesky LinkedIn Reddit Hacker News
Saurabh Atreya, Maheswar Bora, Aritra Mukherjee, Abhijit Das

Computer Vision and Pattern Recognition · Submitted 5 Jan 2024

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim A detailed benchmarking of the proposed dataset using existing techniques and proposed CNN on existing and proposed dataset exhibit the effectiveness of our methodology.

how it is stated · asserted 1.00 read the record →

Comment (0) Read on arXiv ↗
Cite ▾
arXiv ID2401.02649 (version 1)
Pith IDCH3ZGBM2, Pith's permanent record of this paper and its review
CitationAbhijit Das, Aritra Mukherjee, Maheswar Bora, Saurabh Atreya. "Enhancing 3D-Air Signature by Pen Tip Tail Trajectory Awareness: Dataset and Featuring by Novel Spatio-temporal CNN." arXiv:2401.02649 (2024). https://pith.science/paper/CH3ZGBM2
Claim paper

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

Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,771 input tokens.

What the abstract itself states, as probabilities: claims first 0.09 · states limitation 0.06 · human participants 0.96 · controlled comparison 0.59 · randomized assignment 0.01 · states quantitative result 0.82 · compares with prior results 0.89 · mentions code or data availability 0.07.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

This work proposes a novel process of using pen tip and tail 3D trajectory for air signature. To acquire the trajectories we developed a new pen tool and a stereo camera was used. We proposed SliT-CNN, a novel 2D spatial-temporal convolutional neural network (CNN) for better featuring of the air signature. In addition, we also collected an air signature dataset from $45$ signers. Skilled forgery signatures per user are also collected. A detailed benchmarking of the proposed dataset using existing techniques and proposed CNN on existing and proposed dataset exhibit the effectiveness of our methodology.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 This work proposes a novel process of using pen tip and tail 3D trajectory for air signature. contribution 0.99 0.86 0.12 asserted 0.98
s1 To acquire the trajectories we developed a new pen tool and a stereo camera was used. method 0.99 0.95 0.02 asserted 1.00
s2 We proposed SliT-CNN, a novel 2D spatial-temporal convolutional neural network (CNN) for better featuring of the air signature. contribution 0.60 0.94 0.08 asserted 0.99
s3 In addition, we also collected an air signature dataset from $45$ signers. method 0.55 0.97 0.03 asserted 1.00
s4 Skilled forgery signatures per user are also collected. method 0.63 0.91 0.02 asserted 0.99
s5 A detailed benchmarking of the proposed dataset using existing techniques and proposed CNN on existing and proposed dataset exhibit the effectiveness of our methodology. result 0.76 0.92 0.77 asserted 1.00

What kind of paper the classifier reports this is: method or tool 0.71 · evidence from observation or experiment 1.00.

What was examined, and what was not

sources read
  • arXiv listing abstract v1
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

This work proposes a novel process of using pen tip and tail 3D trajectory for air signature. To acquire the trajectories we developed a new pen tool and a stereo camera was used. We proposed SliT-CNN, a novel 2D spatial-temporal convolutional neural network (CNN) for better featuring of the air signature. In addition, we also collected an air signature dataset from $45$ signers. Skilled forgery signatures per user are also collected. A detailed benchmarking of the proposed dataset using existing techniques and proposed CNN on existing and proposed dataset exhibit the effectiveness of our methodology.

Human review

How Pith checks papers → · Full audit of this paper →

Discussion

Know something about this paper? Leave a comment, or highlight any passage above to comment on it.