论文标题
基于图像处理的场景文本检测和识别Tesseract
Image Processing Based Scene-Text Detection and Recognition with Tesseract
论文作者
论文摘要
文本识别是具有相当实际兴趣的计算机视觉的挑战性任务之一。光学特征识别（OCR）可实现自动化的不同应用。该项目着重于自然图像中的单词检测和识别。与阅读扫描文档中的文本相比，目标问题更具挑战性。焦点的用例促进了由于在约束下的图像的可用性，因此可以更准确地检测自然场景中的文本区域的可能性。这是使用安装在卡车上的摄像机捕获同样的图像的相机来实现的。然后使用Tesseract OCR发动机识别检测到的文本区域。即使它受益于低计算能力要求，该模型仅限于特定用例。本文讨论了在测试时发生的关键假阳性情况，并阐述了缓解问题的策略。该项目的正确性格识别率超过80 \％。本文概述了该项目的发展阶段，主要挑战和一些有趣的发现。
Text Recognition is one of the challenging tasks of computer vision with considerable practical interest. Optical character recognition (OCR) enables different applications for automation. This project focuses on word detection and recognition in natural images. In comparison to reading text in scanned documents, the targeted problem is significantly more challenging. The use case in focus facilitates the possibility to detect the text area in natural scenes with greater accuracy because of the availability of images under constraints. This is achieved using a camera mounted on a truck capturing likewise images round-the-clock. The detected text area is then recognized using Tesseract OCR engine. Even though it benefits low computational power requirements, the model is limited to only specific use cases. This paper discusses a critical false positive case scenario occurred while testing and elaborates the strategy used to alleviate the problem. The project achieved a correct character recognition rate of more than 80\%. This paper outlines the stages of development, the major challenges and some of the interesting findings of the project.
