http://www.nitrc.org/forum/forum.php?forum_id=8179 <h3 class="a-plus-plus">Abstract</h3> <p class="a-plus-plus">How to read Uyghur text from biomedical graphic images is a challenge problem due to the complex layout and cursive writing of Uyghur. In this paper, we propose a system that extracts text from Uyghur biomedical images, and matches the text in a specific lexicon for semantic analysis. The proposed system possesses following distinctive properties: first, it is an integrated system which firstly detects and crops the Uyghur text lines using a single fully convolutional neural network, and then keywords in the lexicon are matched by a well-designed matching network. Second, to train the matching network effectively an online sampling method is applied, which generates synthetic data continually. Finally, we propose a GPU acceleration scheme for matching network to match a complete Uyghur text line directly rather than a single window. Experimental results on benchmark dataset show our method achieves a good performance of F-measure 74.5<em class="a-plus-plus">%</em>. Besides, our system keeps high efficiency with 0.5s running time for each image due to the GPU acceleration scheme.</p> en-us Copyright 2000-2026 NITRC OSI Sun, 19 Apr 2026 20:47:52 GMT http://blogs.law.harvard.edu/tech/rss NITRC RSS generator