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Abstract In the current English semantic recognition of mail, there are problems such as serious information distortion and unrecognizable, which affects the promotion of machine automation to recognize text in emails. This study combines the actual situation of the mail image to set the corresponding image processing algorithm, adopts the conversi...
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Citations
... Meanwhile, machine translation can combine with image processing technology (e.g., a camera and optical character recognition tool) to translate signs or handwritten notes, such as the addresses on envelopes (Wen 2019). This is similar to the way the translation circuit approaches the translation of written material, such as in "The Fires of Pompeii" (2008), when the Doctor takes his new companion Donna to ancient Italy. ...
In this chapter, we explore parallels and disparities between the TARDIS’s ‘translation circuit’ and automatic machine translation systems. To do so, we first describe how and why translation was introduced in Doctor Who before comparing this to the ideological and socio-political contexts in which machine translation was first introduced in the period immediately following World War II. We then trace the arc of machine translation development from those early post-war pioneering efforts through several paradigm shifts in which the underlying approach to machine translation changed from being linguistics-based (e.g., using large dictionaries and grammatical rules) to being data-driven (e.g., using statistical probabilities to calculate a likely translation), before arriving at the current state-of-the-art that integrates artificial intelligence (e.g., machine learning). At each step, we explore similarities and differences between the TARDIS’s ‘translation circuit’ and machine translation systems, including how these two tools process languages with various morphological or syntactic characteristics, how the two tools handle challenges such as signed languages, humour, or sensitive language, and what causes each of these tools to break down and offer only partial or no translations. Finally, we conclude that, for the moment, the ‘translation circuit’ and machine translation have more differences than similarities, but who knows what the future holds?
... Nowadays, the detection and recognition of scene texts have become important topics in machine learning and computer vision areas due to the daily use of digital cameras and the huge amount of applications related to this field, such as mobile and context-aware services [60], traffic sign detection [10], [11], image retrieval [58], blind person assistance [61], and text translation [53]. In fact, new applications are still emerging, for example, those related to the interpretation of scene textual content ( Figure 1). ...
Scene text detection has become an important field in the computer vision area due to the increasing number of applications. This is a very challenging problem as textual elements are commonly found in “noisy” and complex natural scenes. Another issue refers to the presence of texts encoded into different languages within the same image. State-of-the-art solutions rely on the use of deep neural network approaches or even ensembles of them. However, such solutions are associated with “heavy” models, which are computationally expensive in terms of memory and storage footprints, which hampers their use in real-time mobile applications. In this work, we introduce Pelee-Text++, a lightweight neural network architecture for multi-lingual multi-oriented scene text detection, especially tailored to running on devices with computational restrictions. Additionally, to the best of our knowledge, this is the first work to evaluate the performance of text detection methods in commercial smartphones. Over this scenario, Pelee-Text++ processes 2.94 frames per second and it is the only evaluated approach that did not cause memory issues on smartphones, even using an input image of 1024×1024 pixels. Our proposal achieves a promising trade-off between efficiency and effectiveness, with a model size of 27 Megabytes and F-measure of 91.20%, 85.78%, 81.72%, 80.30%, 82.53% and 66.51% on ICDAR 2011, ICDAR 2013, ICDAR 2015, MSRA-TD500, ReCTS 2019 and Multi-lingual 2019 datasets, respectively.
With the improvement of people’s living standards, people pay more and more attention to the harm of fungi. Inspired by the smartphone-based aflatoxin rapid detection system proposed by Zhang Liyong, this paper improves the method to a certain extent. First, the average value of the blue component was added to Zhang’s dynamic method to reduce the effect of excess blue component caused by UV light exposure, which was found to mark the white light in the picture. Therefore, the average value of red is introduced to exclude the influence of white light. When this method is applied to single chip microcomputer, it is found that the image produced by single chip microcomputer has great noise. Therefore, median filtering is added to eliminate noise, so as to achieve the purpose of single chip identification.KeywordsFungusUltraviolet lampAflatoxinpicture processingSingle chip microcomputer
