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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及其定义语言的翻译

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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,在低成本产品中捕获设备的经济形象的可用性,如移动电话,导致研究人员对识别图像中的文本的问题引起了重大关注。与印刷文件的识别相比,识别场景文本是一个具有挑战性的问题。在这项工作中,提出了一种新的方法来识别复杂背景自然场景中的文本,从识别的文本,拼写检查和字翻译到用户定义的语言中,最后将翻译在图像上。所提出的方法对不同种类的文本外观具有强大,包括字体大小,字体样式,颜色和背景。结合不同互补技术的各个优势并克服其缺点,所提出的方法使用高效的字符检测和定位技术和多字符分类器准确识别文本。该方法成功地识别自然场景图像上的文本,不依赖于特定的字母表文本背景。它适用于各种各样的字符,可以有效地处理高达20度的偏斜。

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