A HamNoSys-Guided Dataset and Baselines for Fine-Grained Isolated Handshape Recognition in Sign Language

• URL: http://arxiv.org/abs/2608.10588v1
• Date: Tue, 11 Aug 2026 07:16:52 GMT
• Title: A HamNoSys-Guided Dataset and Baselines for Fine-Grained Isolated Handshape Recognition in Sign Language
• Abstract summary: Fine-grained handshape recognition supports computational sign-language transcription, recognition, and translation.<n>This work introduces a benchmark grounded in the language-independent Hamburg Notation System (HamNoSys)
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Purpose: Fine-grained handshape recognition supports computational sign-language transcription, recognition, and translation, but broad, phonetically defined visual inventories with signer-aware evaluation remain limited. This work introduces a benchmark grounded in the language-independent Hamburg Notation System (HamNoSys). Methods: A balanced dataset of 144,000 RGB images was collected from 15 participants for 160 handshape classes defined by the official HamNoSys 4 Handshapes Chart. ResNet-18 and ViT-B/16 were evaluated as appearance-based models, while a graph convolutional network and XGBoost were evaluated from hand landmarks. Both a class-stratified subject-dependent split and a 15-fold leave-one-subject-out (LOSO) protocol were used. The same model families were additionally assessed on LSWH100 and ASL Fingerspelling Dataset A for external context. Results: The subject-dependent benchmarks established reproducible reference performance across all four model families, whereas LOSO evaluation exposed a substantial reduction when recognition was required to generalise to unseen participants. On ASL Fingerspelling Dataset A, mean LOSO top-1 accuracy ranged from 82.20% to 87.40%. Conclusion: The documented acquisition, curation, and complementary evaluation protocols pro-vide a reproducible resource for fine-grained isolated-handshape research and for developing more accessible sign-language technologies.

Related papers

• RPC-Bench: A Fine-grained Benchmark for Research Paper Comprehension [65.81339691942757]
  RPC-Bench is a large-scale question-answering benchmark built from review-rebuttal exchanges of high-quality computer science papers.<n>We design a fine-grained taxonomy aligned with the scientific research flow to assess models' ability to understand and answer why, what, and how questions in scholarly contexts.
  arXiv  Detail & Related papers  (2026-01-14T11:37:00Z)
• VGG Induced Deep Hand Sign Language Detection [0.0]
  This work proposes a novel hand gesture recognizing system for the differently-abled persons.<n>The model uses a convolutional neural network, known as VGG-16 net, for building a trained model on a widely used image dataset.<n>The experimental results show that by combining a transfer learning mechanism together with the image data augmentation, the VGG-16 net produced around 98% accuracy.
  arXiv  Detail & Related papers  (2026-01-13T06:39:29Z)
• Human Cognitive Benchmarks Reveal Foundational Visual Gaps in MLLMs [65.93003087656754]
  VisFactor is a benchmark that digitizes 20 vision-centric subtests from a well-established cognitive psychology assessment.<n>We evaluate 20 frontier Multimodal Large Language Models (MLLMs) from GPT, Gemini, Claude, LLaMA, Qwen, and SEED families.<n>The best-performing model achieves a score of only 25.19 out of 100, with consistent failures on tasks such as mental rotation, spatial relation inference, and figure-ground discrimination.
  arXiv  Detail & Related papers  (2025-02-23T04:21:32Z)
• Hierarchical Windowed Graph Attention Network and a Large Scale Dataset for Isolated Indian Sign Language Recognition [0.20075899678041528]
  We introduce a large-scale isolated ISL dataset and a novel SL recognition model based on skeleton graph structure. The dataset covers 2002 daily used common words in the deaf community recorded by 20 (10 male and 10 female) deaf adult signers. We propose a SL recognition model namely Hierarchical Windowed Graph Attention Network (HWGAT) by utilizing the human upper body skeleton graph.
  arXiv  Detail & Related papers  (2024-07-19T11:48:36Z)
• PenSLR: Persian end-to-end Sign Language Recognition Using Ensembling [0.953605234706973]
  Pen SLR is a glove-based sign language system consisting of an Inertial Measurement Unit (IMU) and five flexible sensors powered by a deep learning framework. We propose a novel ensembling technique by leveraging a multiple sequence alignment algorithm known as Star Alignment. Our evaluations show that Pen SLR achieves a remarkable word accuracy of 94.58% and 96.70% in subject-independent and subject-dependent setups.
  arXiv  Detail & Related papers  (2024-06-24T07:59:34Z)
• Detecting and recognizing characters in Greek papyri with YOLOv8, DeiT and SimCLR [9.7902367664742]
  This paper discusses our submission to the ICDAR 2023 Competition on Detection and Recognition of Greek Letters on Papyri' We used an ensemble of YOLOv8 models to detect and classify individual characters and employed two different approaches for refining the character predictions. Our submission won the recognition challenge with a mAP of 42.2%, and was runner-up in the detection challenge with a mean average precision (mAP) of 51.4%.
  arXiv  Detail & Related papers  (2024-01-23T06:08:00Z)
• Telling Left from Right: Identifying Geometry-Aware Semantic Correspondence [80.6840060272386]
  This paper identifies the importance of being geometry-aware for semantic correspondence. We show that incorporating this information can markedly enhance semantic correspondence performance. Our method achieves a PCK@0.10 score of 65.4 (zero-shot) and 85.6 (supervised) on the challenging SPair-71k dataset.
  arXiv  Detail & Related papers  (2023-11-28T18:45:13Z)
• Handwritten Stenography Recognition and the LION Dataset [0.0]
  Stenographic domain knowledge is integrated by applying four different encoding methods. Test error rates are reduced significantly by combining stenography-specific target sequence encodings with pre-training and fine-tuning.
  arXiv  Detail & Related papers  (2023-08-15T14:25:53Z)
• VisIT-Bench: A Benchmark for Vision-Language Instruction Following Inspired by Real-World Use [49.574651930395305]
  VisIT-Bench is a benchmark for evaluation of instruction-following vision-language models. Our dataset comprises 592 test queries, each with a human-authored instruction-conditioned caption. We quantify quality gaps between models and references using both human and automatic evaluations.
  arXiv  Detail & Related papers  (2023-08-12T15:27:51Z)
• Gait Recognition in the Wild: A Large-scale Benchmark and NAS-based Baseline [95.88825497452716]
  Gait benchmarks empower the research community to train and evaluate high-performance gait recognition systems. GREW is the first large-scale dataset for gait recognition in the wild. SPOSGait is the first NAS-based gait recognition model.
  arXiv  Detail & Related papers  (2022-05-05T14:57:39Z)
• Reliable Evaluations for Natural Language Inference based on a Unified Cross-dataset Benchmark [54.782397511033345]
  Crowd-sourced Natural Language Inference (NLI) datasets may suffer from significant biases like annotation artifacts. We present a new unified cross-datasets benchmark with 14 NLI datasets and re-evaluate 9 widely-used neural network-based NLI models. Our proposed evaluation scheme and experimental baselines could provide a basis to inspire future reliable NLI research.
  arXiv  Detail & Related papers  (2020-10-15T11:50:12Z)
• MOCHA: A Dataset for Training and Evaluating Generative Reading Comprehension Metrics [55.85042753772513]
  We introduce a benchmark for training and evaluating generative reading comprehension metrics: MOdeling Correctness with Human. s. Using MOCHA, we train a Learned Evaluation metric for Reading Pearson, LERC, to mimic human judgement scores. LERC outperforms baseline metrics by 10 to 36 absolute points on held-out annotations. When we evaluate on minimal pairs, LERC achieves 80% accuracy, outperforming baselines by 14 to 26 absolute percentage points while leaving significant room for improvement.
  arXiv  Detail & Related papers  (2020-10-07T20:22:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.