使用 yolov8 计算机视觉模型进行希伯来字母检测与楔形文字泥板分类
Hebrew letters Detection and Cuneiform tablets Classification by using the yolov8 computer vision model
AI总结：
针对希伯来字符标注数据稀缺问题，该研究利用现有音译与拉丁字符逐符号表示自动定位符号，并基于 Yolov8 预训练模型训练检测器，实现希伯来字母识别和楔形文字泥板分类。
摘要：
楔形文字是一种古老的艺术形式，使我们得以窥见过去。除埃及象形文字外，楔形文字是最古老的书写系统之一。许多历史学家认为希伯来语起源于古代。例如，我们曾使用相同的方法破译楔形文字语言；在学会如何破译一种古老语言之后，我们会拜访考古学家，以学习如何破译任何其他古代语言。我们提出一种基于深度学习的符号检测器方法，以加快这一过程，从而根据希伯来字母内容识别并分组楔形文字泥板图像。众所周知，希伯来字母表很难且成本高昂，难以收集深度学习所需的训练数据，这需要用框标出希伯来字符。我们利用现有的音译，以及泥板内容按符号逐符号表示的拉丁字符来解决这个问题。由于监督方法不包含符号定位，我们推荐其中一种：通过将泥板照片中的音译符号与其对应的音译进行比较来找到这些符号。随后，使用这些已定位的符号重新训练符号检测器，而不是利用标注。之后，更有效的符号检测器会提高对齐质量。因此，本研究旨在使用 Yolov8 目标识别预训练模型来识别希伯来字符并对楔形文字泥板进行分类。
英文摘要：
Cuneiform writing, an old art style, allows us to see into the past. Aside from Egyptian hieroglyphs, the cuneiform script is one of the oldest writing systems. Many historians place Hebrew's origins in antiquity. For example, we used the same approach to decipher the cuneiform languages; after learning how to decipher one old language, we would visit an archaeologist to learn how to decipher any other ancient language. We propose a deep-learning-based sign detector method to speed up this procedure to identify and group cuneiform tablet images according to Hebrew letter content. The Hebrew alphabet is notoriously difficult and costly to gather the training data needed for deep learning, which entails enclosing Hebrew characters in boxes. We solve this problem using pre-existing transliterations and a sign-by-sign representation of the tablet's content in Latin characters. We recommend one of the supervised approaches because these do not include sign localization: We Find the transliteration signs in the tablet photographs by comparing them to their corresponding transliterations. Then, retrain the sign detector using these localized signs instead of utilizing annotations. Afterward, a more effective sign detector enhances the alignment quality. Consequently, this research aims to use the Yolov8 object identification pretraining model to identify Hebrew characters and categorize the cuneiform tablets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
