论文标题
面对印刷孟加拉文本图像的光学角色分割的约束
Confronting the Constraints for Optical Character Segmentation from Printed Bangla Text Image
论文作者
论文摘要
在数字化的世界中，光学特征识别具有书面历史的自动化。光学字符识别系统基本上将印刷图像转换为可编辑的文本，以更好地存储和可用性。为了完全发挥作用，系统需要采用一些关键方法，例如预处理和分割。预处理有助于印刷数据无噪声，并有效地摆脱偏度，而细分有助于将图像片段插入线，单词和角色，以便更好地转换。这些步骤使得更好的准确性和一致的结果，以便为印刷图像准备转换。我们提出的算法能够从理想和非理想案例的扫描或捕获图像案例中细分角色，从而获得可持续的结果。这里提供了我们工作的实施：https：//cutt.ly/rgdfbia
In a world of digitization, optical character recognition holds the automation to written history. Optical character recognition system basically converts printed images into editable texts for better storage and usability. To be completely functional, the system needs to go through some crucial methods such as pre-processing and segmentation. Pre-processing helps printed data to be noise free and gets rid of skewness efficiently whereas segmentation helps the image fragment into line, word and character precisely for better conversion. These steps hold the door to better accuracy and consistent results for a printed image to be ready for conversion. Our proposed algorithm is able to segment characters both from ideal and non-ideal cases of scanned or captured images giving a sustainable outcome. The implementation of our work is provided here: https://cutt.ly/rgdfBIa
