Seals are small coin-shaped artifacts, mostly made of lead, held with strings to seal letters. This work presents the first attempt towards automatic reading of text on Byzantine seal images.Byzantine seals are generally decorated with iconography on the obverse side and Greek text on the reverse side. Text may include the sender's name, position in the Byzantine aristocracy, and elements of prayers. Both text and iconography are precious literary sources that wait to be exploited electronically, so the development of computerized systems for interpreting seals images is of paramount importance. This work's contribution is hence a deep, two-stages, character reading pipeline for transcribing Byzantine seal images. A first deep convolutional neural network (CNN) detects characters in the seal (character localization). A second convolutional network reads the localized characters (character classification). Finally, a diplomatic transcription of the seal is provided by post-processing the two network outputs. We provide an experimental evaluation of each CNN in isolation and both CNNs in combination. All performances are evaluated by cross-validation. Character localization achieves a mean average precision ([email protected]) greater than 0.9. Classification of characters cropped from ground truth bounding boxes achieves Top-1 accuracy greater than 0.92. End-to-end evaluation shows the efficiency of the proposed approach when compared to the SoTA for similar tasks.

翻译：印章是一种小型硬币状文物，多用铅制成，以绳系于信件封口处。本研究首次尝试对拜占庭印章图像进行文本自动识读。拜占庭印章正面通常装饰有圣像图案，背面则刻有希腊文文本。文本内容可能包含发件人姓名、在拜占庭贵族体系中的职位以及祷词元素。文本与圣像图案均属亟待电子化开发的珍贵文献资源，因此开发计算机化印章图像解读系统具有至关重要的意义。本文的贡献在于提出一种深度双阶段字符识别流水线，用于转写拜占庭印章图像：第一阶段采用深度卷积神经网络（CNN）检测印章中的字符（字符定位），第二阶段通过另一卷积网络对定位到的字符进行识读（字符分类）。最终通过对两个网络输出结果进行后处理，生成印章的外交式转写文本。我们分别对单个CNN及两CNN组合进行了实验评估，所有性能指标均通过交叉验证测定。字符定位的平均精度均值（[email protected]）超过0.9，基于真实边框裁剪字符的分类Top-1准确率超过0.92。端到端评估表明，与同类任务的最新技术（SoTA）相比，本文方法具有显著效能。