Historical manuscripts and archival documentation are handwritten texts which are the backbone sources for historical inquiry. Recent developments in the digital humanities field and the need for extracting information from the historical documents have fastened the digitization processes. Cutting edge machine learning methods are applied to extract meaning from these documents. Page segmentation (layout analysis), keyword, number and symbol spotting, handwritten text recognition algorithms are tested on historical documents. For most of the languages, these techniques are widely studied and high performance techniques are developed. However, the properties of Arabic scripts (i.e., diacritics, varying script styles, diacritics, and ligatures) create additional problems for these algorithms and, therefore, the number of research is limited. In this research, we first automatically spotted the Arabic numerals from the very first series of population registers of the Ottoman Empire conducted in the mid-nineteenth century and recognized these numbers. They are important because they held information about the number of households, registered individuals and ages of individuals. We applied a red color filter to separate numerals from the document by taking advantage of the structure of the studied registers (numerals are written in red). We first used a CNN-based segmentation method for spotting these numerals. In the second part, we annotated a local Arabic handwritten digit dataset from the spotted numerals by selecting uni-digit ones and tested the Deep Transfer Learning method from large open Arabic handwritten digit datasets for digit recognition. We achieved promising results for recognizing digits in these historical documents.

中文翻译：

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在奥斯曼帝国人口登记簿中使用深度迁移学习自动基于CNN的阿拉伯数字斑点和手写数字识别

历史手稿和档案文献是手写文本，是历史查询的主要来源。数字人文科学领域的最新发展以及从历史文献中提取信息的需求已加快了数字化进程。应用最先进的机器学习方法从这些文档中提取含义。页面分段（布局分析），关键字，数字和符号点，手写文本识别算法均在历史文档上进行了测试。对于大多数语言，这些技术已得到广泛研究，并且开发了高性能技术。但是，阿拉伯文字的属性（即变音符号，变化的文字样式，变音符号和连字）为这些算法带来了其他问题，因此研究数量有限。在这项研究中，我们首先自动从19世纪中叶进行的奥斯曼帝国第一批人口登记册中发现了阿拉伯数字，并识别了这些数字。它们之所以重要，是因为它们拥有有关家庭数量，注册个人和个人年龄的信息。通过利用已研究寄存器的结构（数字用红色书写），我们将红色滤光片用于将数字与文档分开。我们首先使用基于CNN的分割方法来发现这些数字。在第二部分中，我们通过选择单个数字来标注斑点数字中的本地阿拉伯手写数字数据集，并从大型开放式阿拉伯手写数字数据集中测试了深度迁移学习方法以进行数字识别。