A Gaussian Process Upsampling Model for Improvements in Optical Character Recognition
A Gaussian Process Upsampling Model for Improvements in Optical Character Recognition
复制标题
用于改进光学字符识别的高斯过程上采样模型
DOI：
10.1007/978-3-030-64559-5_20
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Verma, Kunal
中科院分区：
文献类型：
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作者：
Reeves, Steven;Lee, Dongwook;Singh, Anurag;Verma, Kunal
财务单据的自动评估和提取是提高业务效率的关键环节。提取的大部分依赖于光学字符识别(OCR)，其结果取决于文档图像的质量。馈送到自动化系统的图像数据的质量可能不可靠、固有的低分辨率或被传输程序下采样和压缩。本文提出了一种新的高斯过程(GP)上采样模型，用于改进OCR过程和通过对低分辨率文档进行上采样来进行提取。
The automatic evaluation and extraction of financial documents is a key process in business efficiency. Most of the extraction relies on the Optical Character Recognition (OCR), whose outcome is dependent on the quality of the document image. The image data fed to the automated systems can be of unreliable quality, inherently low-resolution or downsampled and compressed by a transmitting program. In this paper, we illustrate a novel Gaussian Process (GP) upsampling model for the purposes of improving OCR process and extraction through upsampling low resolution documents.