Titlebook: Intermodale Wettbewerbsdynamik im europ?ischen Personenverkehr; Bestimmungsfaktoren Caroline Porwoll Book 2007 Springer Fachmedien Wiesbad

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Caroline Porwollor text removal from images. TPFNet has two parts: feature synthesis and image generation. Since noise can be more effectively removed from low-resolution images, part 1 operates on low-resolution images. Part 1 uses PVT or EfficientNet-B6 as the encoder. Further, we use a novel multi-headed decoder

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Caroline Porwollever, the independence of the dictionary on the visual features may lead to incorrect rectification of accurate visual predictions. In this paper, we propose a new dictionary language model leveraging the .cene .mage-.ext .atching(SITM) network, which avoids the drawbacks of the explicit dictionary

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Caroline Porwoll is that a word or character is correctly determined. The practical application of this score is not yet clear and various studies have discussed the (un)usability of these confidence score as an estimation of OCR quality. Using a dataset of 2000 historical Dutch newspapers we investigated different

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Caroline Porwolle interactions between different symbols, which may fail when faced similar symbols. To alleviate this issue, we propose a simple but efficient method to enhance semantic interaction learning (SIL). Specifically, we firstly construct a semantic graph based on the statistical symbol co-occurrence pro

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d, we propose a multi-level synthesis strategy to synthesize the corresponding handwritten equations from LaTeX expressions and regard the chemical equation recognition as an image-to-markup task. In particular, our approach first decomposes the LaTeX expression into a symbol layout tree (SLT) and o

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