论文标题
使用文本分割和隐藏的马尔可夫模型直接在压缩域中的TIFF压缩文档图像的OCR直接在压缩域中
OCR for TIFF Compressed Document Images Directly in Compressed Domain Using Text segmentation and Hidden Markov Model
论文作者
论文摘要
在当今的技术时代，文档图像在我们的日常生活中起着重要而重要的作用，尤其是在Covid-19的激增中，数字扫描的文档已成为关键的交流来源，因此通过身体接触避免了任何形式的感染。扫描文档图像的存储和传输是一项非常密集的任务，因此，正在使用压缩技术来减少档案和传输之前的图像大小。要提取信息或在压缩图像上操作，我们有两种方法。第一种方法是对图像进行解压缩并在其上进行操作，然后再次压缩它以提高存储和传输效率。另一种方法是使用基础压缩算法的特征来直接以压缩形式处理图像，而无需减压和重压。在本文中，我们提出了一个新颖的想法，即为CCITT（国际电报和电话咨询委员会）压缩机器打印的TIFF文档图像直接在压缩域中为CCITT开发OCR。将文本区域分为线和单词后，使用CCITT-CCITT-Horizontal，垂直和通过模式的三种编码模式将HMM应用于识别。实验结果表明，通过模式上的OCR给出了有希望的结果。
In today's technological era, document images play an important and integral part in our day to day life, and specifically with the surge of Covid-19, digitally scanned documents have become key source of communication, thus avoiding any sort of infection through physical contact. Storage and transmission of scanned document images is a very memory intensive task, hence compression techniques are being used to reduce the image size before archival and transmission. To extract information or to operate on the compressed images, we have two ways of doing it. The first way is to decompress the image and operate on it and subsequently compress it again for the efficiency of storage and transmission. The other way is to use the characteristics of the underlying compression algorithm to directly process the images in their compressed form without involving decompression and re-compression. In this paper, we propose a novel idea of developing an OCR for CCITT (The International Telegraph and Telephone Consultative Committee) compressed machine printed TIFF document images directly in the compressed domain. After segmenting text regions into lines and words, HMM is applied for recognition using three coding modes of CCITT- horizontal, vertical and the pass mode. Experimental results show that OCR on pass modes give a promising results.
