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• W4207066734 abstract "AbstractVarious research initiatives have been reported regarding highly effective results for the text detection problem, which consists of detecting textual elements, such as words and phrases, in digital images. Text localization is an important step on very widely used mobile applications, for instance, on-the-go translations and recognition of text for the visually impaired. At the same time, edge computing is revolutionizing the way embedded systems are architected by moving complex processing and analysis to end devices (e.g., mobile and wearable devices). In this context, the development of lightweight networks that can be run in devices with restricted computing power and with a minimum latency as possible is essential to make plenty of mobile-oriented solutions feasible in practice. In this work, we investigate the use of efficient object detection networks to address this task, proposing the fusion of two lightweight neural network architectures, MobileNetV2 and Single Shot Detector (SSD), into our approach named MobText. As experimental results in the ICDAR’11 and ICDAR’13 datasets demonstrates that our solution yields the best trade-off between effectiveness and efficiency in terms of processing time, achieving the state-of-the-art results on the ICDAR’11 dataset with an F-measure of (96.09%) and an average processing time of 464 ms on a smartphone device, over experiments executed on both dataset images and with images captured in real time from the portable device.KeywordsScene text detectionMobile devicesObject detector networksMobilenetV2Single shot detector" @default.
• W4207066734 created "2022-01-26" @default.
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• W4207066734 date "2022-01-01" @default.
• W4207066734 modified "2023-10-01" @default.
• W4207066734 title "Scene Text Localization Using Lightweight Convolutional Networks" @default.
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• W4207066734 doi "https://doi.org/10.1007/978-3-030-94893-1_13" @default.
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