Uyghur Text Matching in Graphic Images for Biomedical Semantic Analysis

Abstract

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%. Besides, our system keeps high efficiency with 0.5s running time for each image due to the GPU acceleration scheme.

Similarity matching of TCM prescription rational medication based on Siamese network

“…Pang et al [13] proposed the Match Pyramid model, which converts the word vector matrices of two texts into a twodimensional graph form through dot product, and then uses CNN to perform convolutional operations on the two-dimensional graph. Fang et al [14]…”