A Text Recognition Algorithm Based on a Dual-Attention Mechanism in Complex Driving Environment

In response to many problems such as complex background of text recognition environment, perspective distortion, shallow handwriting, and mixed Chinese and English characters, we have designed an OCR algorithm framework with features such as landmark extraction and correction, image enhancement, text detection, and text recognition. We have designed a DBNet based on dual attention mechanism and content-aware upsampling. We have also designed a text recognition module incorporating the central loss CRNN + CTC to improve content awareness. Experimental results show that the improved text detection network in this paper has increased accuracy by 5.09%, recall by 2.12%, and F-score by 3.46% on the ICDAR2015 dataset. The text recognition network has improved the accuracy of recognizing Chinese and English characters by 1.2%

Automatic landmark annotation and dense correspondence registration for 3D human facial images

Dense surface registration of three-dimensional (3D) human facial images holds great potential for studies of human trait diversity, disease genetics, and forensics. Non-rigid registration is particularly useful for establishing dense anatomical correspondences between faces. Here we describe a novel non-rigid registration method for fully automatic 3D facial image mapping. This method comprises two steps: first, seventeen facial landmarks are automatically annotated, mainly via PCA-based feature recognition following 3D-to-2D data transformation. Second, an efficient thin-plate spline (TPS) protocol is used to establish the dense anatomical correspondence between facial images, under the guidance of the predefined landmarks. We demonstrate that this method is robust and highly accurate, even for different ethnicities. The average face is calculated for individuals of Han Chinese and Uyghur origins. While fully automatic and computationally efficient, this method enables high-throughput analysis of human facial feature variation.Comment: 33 pages, 6 figures, 1 tabl

Dinner for three: EU, China and the US around the geographical indications table. CEPS Policy Insights No 2020-07 / April 2020

China is the EU's second biggest agri-food exports market. It is also the second destination for the export of EU products protected by geographical indications (GI), accounting for 9% of its value, including wines, agrifood and spirits. The EU-China Agreement on the Protection of Geographical Indications, concluded in November 2019, is expected to realise higher potential for exporting EU GIs to the country since market access is now guaranteed. But the US-China Economic and Trade Agreement, signed in January 2020, has set down a couple of precautionary measures, including a consultation mechanism with China before new GIs can be recognised for protection in the Chinese market because of international trade agreements. As a result, EU GIs could be brought under tighter US scrutiny before being recognised for protection in China. Analysis reveals, however, that only a handful of EU GIs may be affected by the latter Agreement, if at all