## 1 Introduction  
To preserve and access our cultural heritage, the digitization of historical manuscripts is an important goal for libraries all around the world. Scanning the documents and indexing them with meta-information about author, date, place, etc. is a first step towards this goal. Afterwards, automatic document analysis and recognition is needed to extract texts, illustrations, signatures, stamps, etc. from the scanned page images and make them amenable to searching, browsing, indexing, and linking similar to websites on the Internet. In recent decades, a steady advance in research has made it possible to analyze even highly degraded manuscripts written in ancient languages and scripts [1]. The majority of current methods are based on machine learning and thus have a fundamental limitation --the need for human-annotated learning samples to train the document analysis systems. In the context of automated reading, such learning samples may consist for example of bounding boxes drawn around text lines together with their machine-readable text. Considering the wide variety of historical documents, it is often necessary to manually annotate dozens or hundreds of pages, only to transcribe a few thousand similar pages afterwards with an automatic system. Automatic transcription alignment [2] has been suggested as a promising approach to cope with this limitation for cases where scholars have already created a transcription. In such a case, aligning the words in the image with the words in the transcription not only facilitates browsing of the scanned manuscript but also provides the ground truth information needed to train an automated reading system. Several methods have been put forward to tackle this challenge (see Section 2), some of them based on heuristicsavoiding machine learning altogether and others using weakly trained alignment systems, i.e., systems that require only a few labeled samples for the alignment task, which is simpler than the reading task. In either case, the alignment methods have to provide a solution for two distinct problems. First, for segmenting the image into text elements, and secondly, for aligning the image segments with the text. In this article, we propose an alignment method, which solves the segmentation problem by means of fully convolutional object detection and the alignment problem by means of clustering of the detected bounding boxes. For training the object detection system, we use purely synthetic learning samples based on a gray text background and a printed font. Subsequently, the synthetic detection system is applied to a real manuscript and aligns the transcription. Finally, self-training is performed on the alignment results without human supervision in order to adapt from the gray background to the real page background and from the printed font to the handwriting style. The resulting adapted detection system is an ideal starting point for further document analysis steps, such as keyword spotting and transcription. The proposed transcription alignment method has the following key properties:  
* _Segmentation-free._ The method can be applied directly to the scanned page without image preprocessing, paragraph, line, or word segmentation. * _Annotation-free._ No human-annotated learning sample is required, which is the main contribution of the proposed method. * _Learning-based._ The segmentation problem is addressed with machine learning, which tends to be more robust to variations in the page background and the handwriting style when compared with heuristic methods. To the best of our knowledge, the proposed approach is one of the first that combines the three properties, which greatly facilitate transcription alignment. Please note, however, that a font of the script under consideration is required. Furthermore, the clustering algorithm used to solve the alignment problem has been specifically designed for the dataset at hand, and would need to be adapted when dealing with other types of manuscripts.