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This work details Sighthounds fully automated license plate detection and recognition system. The core technology of the system is built using a sequence of deep Convolutional Neural Networks (CNNs) interlaced with accurate and efficient algorithms. The CNNs are trained and fine-tuned so that they are robust under different conditions (e.g. variations in pose, lighting, occlusion, etc.) and can work across a variety of license plate templates (e.g. sizes, backgrounds, fonts, etc). For quantitative analysis, we show that our system outperforms the leading license plate detection and recognition technology i.e. ALPR on several benchmarks. Our system is available to developers through the Sighthound Cloud API at https://www.sighthound.com/products/cloud
We present an integrated framework for using Convolutional Networks for classification, localization and detection. We show how a multiscale and sliding window approach can be efficiently implemented within a ConvNet. We also introduce a novel deep l
A method to extract and recognize isolated characters in license plates is proposed. In extraction stage, the proposed method detects isolated characters by using Difference-of-Gaussian (DOG) function, The DOG function, similar to Laplacian of Gaussi
There is a warning light for the loss of plant habitats worldwide that entails concerted efforts to conserve plant biodiversity. Thus, plant species classification is of crucial importance to address this environmental challenge. In recent years, the
The growth of high-performance mobile devices has resulted in more research into on-device image recognition. The research problems are the latency and accuracy of automatic recognition, which remains obstacles to its real-world usage. Although the r
Recognizing car license plates in natural scene images is an important yet still challenging task in realistic applications. Many existing approaches perform well for license plates collected under constrained conditions, eg, shooting in frontal and