EfficientSign: An Attention-Enhanced Lightweight Architecture for Indian Sign Language Recognition

• URL: http://arxiv.org/abs/2604.08694v1
• Date: Thu, 09 Apr 2026 18:27:57 GMT
• Title: EfficientSign: An Attention-Enhanced Lightweight Architecture for Indian Sign Language Recognition
• Abstract summary: We build a lightweight model which takes EfficientNet-B0 and focuses on two attention modules.<n>We tested it against five other approaches on 12,637 images of Indian Sign Language alphabets.
• Score: 3.4772255133148366
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: How do you build a sign language recognizer that works on a phone? That question drove this work. We built EfficientSign, a lightweight model which takes EfficientNet-B0 and focuses on two attention modules (Squeeze-and-Excitation for channel focus, and a spatial attention layer that focuses on the hand gestures). We tested it against five other approaches on 12,637 images of Indian Sign Language alphabets, all 26 classes, using 5-fold cross-validation. EfficientSign achieves the accuracy of 99.94% (+/-0.05%), which matches the performance of ResNet18's 99.97% accuracy, but with 62% fewer parameters (4.2M vs 11.2M). We also experimented with feeding deep features (1,280-dimensional vectors pulled from EfficientNet-B0's pooling layer) into classical classifiers. SVM achieved the accuracy of 99.63%, Logistic Regression achieved the accuracy of 99.03% and KNN achieved accuracy of 96.33%. All of these blow past the 92% that SURF-based methods managed on a similar dataset back in 2015. Our results show that attention-enhanced learning model provides an efficient and deployable solution for ISL recognition without requiring a massive model or hand-tuned feature pipelines anymore.

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