## 2 Related Work
### Transcription Alignment  
A large number of historical manuscripts have already been transcribed by scholars, but in most cases the transcriptions are not aligned with the scanned manuscript images, i.e., it is not known where on the page the different words and characters are located. Transcription alignment aims to establish such an alignment automatically, which greatly facilitates the creation of historical handwriting datasets. Several alignment methods have been suggested for Latin text written in text lines. Avoiding machine learning entirely, _heuristic transcription alignment approaches_ include the method proposed by [PERSON] et al. [17] for medieval Latin manuscripts, which first performs a gradient-based line segmentation, followed by feature matching between the line images and the Unicode transcription using dynamic programming. Another example is the method proposed by [PERSON] et al. [18] for historical Hebrew manuscripts, which employs scale-space anisotropic smoothing for segmenting the lines and dynamic programming to match the connected components of the line images with a sequence of synthetic prototype characters.  
Heuristic methods are limited in their generalizability when dealing with a certain variability of page layouts, page backgrounds, and handwriting styles. Therefore, several _learning-based transcription alignment approaches_ have been investigated as well, including the hidden Markov model (HMM)-based approach proposed by [PERSON] et al. [19] for medieval Latin manuscripts, which is based on a heuristic line segmentation with seam carving, followed by an HMM-based forced alignment with the Viterbi algorithm. This method specifically addressed the problem of discrepancies between the text visible in the image and the human transcription, especially with a view to abbreviations that are frequent in medieval Latin texts. In a similar method proposed by [PERSON] et al. [20], the heuristic line segmentation is further replaced with an HMM-based approach. Moving from HMM to convolutional approaches, [PERSON] et al. [21] use convolutional neural network (CNN) features in combination with long short-term memory (LSTM) cells and connectionist temporal classification (CTC) to align text line images with their transcription. In their work, line segmentation is achieved heuristically by means of contour distribution analysis. Recently, [PERSON] et al. [22] have proposed the use of fully convolutional object detection for aligning early printed Latin documents. To train a Faster R-CNN architecture, lines and words are first segmented heuristically using binarization and projection profiles. The object detection results are then aligned with the transcription using dynamic programming.  
For modern Chinese handwriting, a transcription alignment method is proposed by [PERSON] et al. [23], using a minimal spanning tree for line segmentation and a statistical classifier, supported by geometric context, for alignment using dynamic programming. Please note that all works mentioned are based on some form of dynamic programming to align part-of-image sequences with text sequences.  
When compared with the alignment approaches mentioned, our method is most closely related to that of [PERSON] et al. [22], because we also base our alignment on object detection. However, we do not require an initial image preprocessing step that segments the scanned page image into text blocks, lines, or words. Instead, we train the object detection method with synthetic page images and apply it to entire scanned pages in order to localize text elements before alignment. Another difference is the alignment algorithm itself, which is not sequential in our case but instead based on two-dimensional clustering into columns and rows.