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Automatic text recognition in natural scene and its translation into user defined language

机译:自然场景中的自动文本识别并将其翻译为用户定义的语言

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摘要

In recent year's availability of economical image capturing devices in low cost products like mobile phones has led a significant attention of researchers to the problem of recognizing text in images. Recognition of scene text is a challenging problem compared to the recognition of printed documents. In this work a novel approach is proposed to recognize text in complex background natural scene, word formation from recognized text, spelling checking and word translation into user defined language and finally overlay translated word onto the image. The proposed approach is robust to different kinds of text appearances, including font size, font style, color, and background. Combining the respective strengths of different complementary techniques and overcoming their shortcomings, the proposed method uses efficient character detection and localization technique and multiclass classifier to recognize the text accurately. The proposed approach successfully recognizes text on natural scene images and does not depend on a particular alphabet, text background. It works with a wide variety in size of characters and can handle up to 20 degree skewness efficiently.
机译:近年来,在低成本产品(如移动电话)中提供了经济的图像捕获设备,这引起了研究人员对图像中文本识别问题的极大关注。与打印文档的识别相比,场景文本的识别是一个具有挑战性的问题。在这项工作中,提出了一种新颖的方法来识别复杂背景自然场景中的文本,从识别的文本形成单词,将拼写检查和单词翻译成用户定义的语言,最后将翻译后的单词覆盖到图像上。所提出的方法对于不同种类的文本外观(包括字体大小,字体样式,颜色和背景)具有鲁棒性。结合不同互补技术各自的长处,克服了它们的不足,该方法利用有效的字符检测定位技术和多分类器来准确识别文本。所提出的方法成功地识别了自然场景图像上的文本,并且不依赖于特定的字母,文本背景。它可以处理各种大小的字符,并且可以有效处理高达20度的偏斜。

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