Transfer Learning with L2 Norm Regularization for classifying static Two Hand Hindi Sign Language Gestures

Jaiswal, Mohita; Sharmay, Vaidehi; Sharmaz, Abhishek; Tomar, Raghuvir Singh

doi:10.1109/csnt48778.2020.9115767

Cited by 9 publications

(1 citation statement)

References 14 publications

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“…Gesture recognition offers a feasible solution for communication between the deaf and hard-of-hearing community and individuals with typical hearing abilities, becoming an essential tool in humancomputer interaction. In recent years, extensive research has been conducted in the field of sign language recognition [1][2][3][4][5][6]. Advances in deep learning (DL) algorithms, particularly convolutional neural networks (CNNs), have played a crucial role in addressing complex tasks such as image recognition [7][8][9][10][11][12] and object detection [13].…”

Section: Introductionmentioning

confidence: 99%

Hardware accelerator for high accuracy sign language recognition with residual network based on FPGAs

Yang,

Li,

Hao

et al. 2024

IEICE Electron. Express

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The ResNet series of networks has demonstrated powerful capabilities in the fields of object detection and image classification, garnering increasing attention from researchers. However, due to their deep network architectures, accelerator development based on FPGA faces challenges associated with limited on-chip resources and lengthy design cycles. This paper presents a versatile hardware acceleration system based on FPGA, achieving optimization through both hardware implementation and software inference architecture. The system reduces network complexity by employing techniques such as inter-layer fusion and dynamic quantization, while enhancing hardware resource utilization through channel parallelism and tightly-pipelined hardware design principles. By configuring and reusing computation units, the forward inference process of ResNet series networks can be rapidly deployed on FPGA, shortening the development and validation cycles. The proposed system is validated using the ResNet-18 model on a PYNQ-Z2 development board within a gesture recognition application scenario. The overall power consumption of the system is 2.136W, with hardware inference accuracy reaching 98.87%.

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