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Performance of Document Image OCR Systems for Recognizing Video Texts on Embedded Platform

机译:用于嵌入式平台上视频文本识别的文档图像OCR系统的性能

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Market demand for an embedded realization of video OCR motivated the authors to exert an attempt to evaluate the performance of existing document image OCR techniques for the same. Thus authors have tried to port the open source OCR systems like GOCR and Tessar act on an embedded platform. But their performance on an embedded platform shows that the character level and word level recognition accuracy is quite unacceptable for video text. This paper compares two such open source OCR systems on Indian TV videos and proposes some techniques that can be used to improve the recognition accuracy from 62% to 93%. Moreover the challenges of porting those codes on an embedded platform is also analyzed in this paper.
机译:对于视频OCR的嵌入式实现的市场需求促使作者进行尝试,以评估现有文档图像OCR技术的性能。因此,作者试图将开放源代码OCR系统(如GOCR和Tessar)移植到嵌入式平台上。但是它们在嵌入式平台上的性能表明,字符级和单词级的识别精度对于视频文本来说是完全不能接受的。本文比较了印度电视视频上的两个此类开源OCR系统,并提出了一些可用于将识别准确率从62%提高到93%的技术。此外,本文还分析了在嵌入式平台上移植这些代码的挑战。

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