中圖分類號： TN911.73,；TP391.4
文獻標(biāo)識碼： A
DOI：10.16157/j.issn.0258-7998.200316
中文引用格式： 陳小順，王良君. 基于分割的自然場景下文本檢測方法與應(yīng)用[J].電子技術(shù)應(yīng)用,，2021,，47(2)：54-57.
英文引用格式： Chen Xiaoshun，Wang Liangjun. Text detection and application in natural scene based on segmentation[J]. Application of Electronic Technique,，2021,，47(2)：54-57.

Text detection and application in natural scene based on segmentation

Abstract： Text recognition in nature scene is currently applied in various intelligence equipment. The first step of text recognition is to precisely locate the text. In the Pixel Link text location methods, there are mainly two problems: too much background information is incorporated in the text region, and the test accuracy is insufficient. Aiming at these issues, an improved text location method was proposed to precisely locate the text in the natural scene. At first, an attention mechanism was incorporated into the original network. By focusing on the weight relationship between feature channels in the generated feature map, one can improve the weight coefficient of effective feature channels, and suppress the weight of inefficient or invalid feature channels. In the second, by changing the form of data set annotation, the coordinate points can be expressed as a series of sequence forms, so that the text lines can be framed adaptively in the LSTM model. At last, the located object is rotated according to the angle between a pair of vertexes in the polygon frame, and is subsequently fed to the text recognition interface to obtain the final character. Finally, the text detection test is carried out on the open data set and self-built data set. The experimental results show that the improved detection method is superior to the original method on different dataset, and the accuracy is similar to the current leading method.

Key words : pixel segmentation；attention mechanism；LSTM,；natural scene text detection