# CSLRConformer: A Data-Centric Conformer Approach for Continuous Arabic Sign Language Recognition on the Isharah Datase

arXiv: https://arxiv.org/abs/2508.01791 · HTML: https://mlsanity.com/paper/2508.01791/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 25 references that are in the map.
- First posted on arXiv 3 Aug 2025.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [Skeleton-Contrastive 3D Action Representation Learning](https://mlsanity.com/paper/2108.03656/) (2021)
- [ArabSign: A Multi-modality Dataset and Benchmark for Continuous Arabic Sign Language Recognition](https://mlsanity.com/paper/2210.03951/) (2022)
- [Isharah: A Large-Scale Multi-Scene Dataset for Continuous Sign Language Recognition](https://mlsanity.com/paper/2506.03615/) (2025)
- [Continuous Sign Language Recognition with Adapted Conformer via Unsupervised Pretraining](https://mlsanity.com/paper/2405.12018/) (2024)

## Abstract (arXiv)

The field of Continuous Sign Language Recognition (CSLR) poses substantial technical challenges, including fluid inter-sign transitions, the absence of temporal boundaries, and co-articulation effects. This paper, developed for the MSLR 2025 Workshop Challenge at ICCV 2025, addresses the critical challenge of signer-independent recognition to advance the generalization capabilities of CSLR systems across diverse signers. A data-centric methodology is proposed, centered on systematic feature engineering, a robust preprocessing pipeline, and an optimized model architecture. Key contributions include a principled feature selection process guided by Exploratory Data Analysis (EDA) to isolate communicative keypoints, a rigorous preprocessing pipeline incorporating DBSCAN-based outlier filtering and spatial normalization, and the novel CSLRConformer architecture. This architecture adapts the hybrid CNN-Transformer design of the Conformer model, leveraging its capacity to model local temporal dependencies and global sequence context; a characteristic uniquely suited for the spatio-temporal dynamics of sign language. The proposed methodology achieved a competitive performance, with a Word Error Rate (WER) of 5.60% on the development set and 12.01% on the test set, a result that secured a 3rd place ranking on the official competition platform. This research validates the efficacy of cross-domain architectural adaptation, demonstrating that the Conformer model, originally conceived for speech recognition, can be successfully repurposed to establish a new state-of-the-art performance in keypoint-based CSLR.