Segmentation-Less Extraction of Text and Non-Text Regions From JPEG 2000 Compressed Document Images Through Partial and Intelligent Decompression
Segmentation-Less Extraction of Text and Non-Text Regions From JPEG 2000 Compressed Document Images Through Partial and Intelligent Decompression
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DOI：
10.1109/access.2023.3249961
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发表时间：
2023
期刊：
影响因子：
3.9
通讯作者：
Tejasvee Bisen;M. Javed;P. Nagabhushan;Osamu Watanabe
中科院分区：
文献类型：
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作者：
Tejasvee Bisen;M. Javed;P. Nagabhushan;Osamu Watanabe
JPEG 2000是一种流行的图像压缩技术，它使用离散小波变换（DWT）进行压缩，并随后提供了许多丰富的功能，用于有效的存储和解压缩。尽管压缩图像对于存档和通信目的是优选的，但是由于解压缩和重新压缩操作的开销，它们的处理变得困难，所述解压缩和重新压缩操作需要与数据需要操作的次数一样多的次数。因此，在本研究论文中，提出了一种新的思想，直接操作的JPEG 2000压缩文件提取文本和非文本区域，而不使用任何分割算法。与传统方法相比，该技术避免了对压缩文档的完全解压缩，在传统方法中，它们完全解压缩然后处理。此外，JPEG 2000的功能，探索在这项研究工作中，部分和智能化的选择感兴趣的区域在不同的分辨率和位深度，以实现无分割提取的文本和非文本区域。最后利用最大稳定极值区域（MSER）算法提取分割后的文本和非文本区域的版面，以供进一步分析。在标准的PRImA布局分析数据集上进行了实验，得到了令人满意的结果，节省了计算资源。
JPEG 2000 is a popular image compression technique that uses Discrete Wavelet Transform (DWT) for compression and subsequently provides many rich features for efficient storage and decompression. Though compressed images are preferred for archival and communication purposes, their processing becomes difficult due to the overhead of decompression and re-compression operations which are needed as many times the data needs to operate. Therefore in this research paper, the novel idea of direct operation over the JPEG 2000 compressed documents is proposed for extracting text and non-text regions without using any segmentation algorithm. The technique avoids full decompression of the compressed document in contrast to the conventional methods, where they fully decompress and then process. Moreover, JPEG 2000 features are explored in this research work to partially and intelligently decompress only the selected regions of interest at different resolutions and bitdepths to accomplish segmentation-less extraction of text and non-text regions. Finally Maximally Stable Extremal Regions (MSER) algorithm is used to extract the layout of segmented text and non-text regions for further analysis. Experiments have been carried out on the standard PRImA Layout Analysis Dataset leading to promising results and saving computational resources.