De Gregorio Giuseppe, Capriolo Giuliana, Marcelli Angelo
Department of Information and Electrical Engineering and Applied Mathematics, University of Salerno, Via Giovanni Paolo II, 132, 84084 Fisciano, Italy.
Department of Cultural Heritage, University of Salerno, Via Giovanni Paolo II, 132, 84084 Fisciano, Italy.
J Imaging. 2023 Jan 13;9(1):17. doi: 10.3390/jimaging9010017.
The growth of digital libraries has yielded a large number of handwritten historical documents in the form of images, often accompanied by a digital transcription of the content. The ability to track the position of the words of the digital transcription in the images can be important both for the study of the document by humanities scholars and for further automatic processing. We propose a learning-free method for automatically aligning the transcription to the document image. The method receives as input the digital image of the document and the transcription of its content and aims at linking the transcription to the corresponding images within the page at the word level. The method comprises two main original contributions: a line-level segmentation algorithm capable of detecting text lines with curved baseline, and a text-to-image alignment algorithm capable of dealing with under- and over-segmentation errors at the word level. Experiments on pages from a 17th-century Italian manuscript have demonstrated that the line segmentation method allows one to segment 92% of the text line correctly. They also demonstrated that it achieves a correct alignment accuracy greater than 68%. Moreover, the performance achieved on widely used data sets compare favourably with the state of the art.
数字图书馆的发展产生了大量以图像形式存在的手写历史文献，其内容通常还伴有数字转录。对于人文学者研究文献以及进一步进行自动处理而言，追踪数字转录中的文字在图像中的位置的能力可能都很重要。我们提出一种无需学习的方法，用于自动将转录内容与文献图像对齐。该方法将文献的数字图像及其内容的转录作为输入，目标是在单词级别将转录与页面内相应的图像进行链接。该方法有两个主要的原创贡献：一种能够检测具有弯曲基线的文本行的行级分割算法，以及一种能够处理单词级别分割不足和过度分割错误的文本到图像对齐算法。对一份17世纪意大利手稿页面的实验表明，行分割方法能正确分割92%的文本行。实验还表明，它实现了大于68%的正确对齐准确率。此外，在广泛使用的数据集上所取得的性能与现有技术相比具有优势。
