Automatic Line Segmentation and Ground-Truth Alignment of Handwritten Documents

In this paper, we present a method for the automatic segmentation and transcript alignment of documents, for which we only have the transcript at the document level. We consider several line segmentation hypotheses, and recognition hypotheses for each segmented line. The recognition is highly constrained with the document transcript. We formalize the problem in a weighted finite-state transducer framework. We evaluate how the constraints help achieve a reasonable result. In particular, we assess the performance of the system both in terms of segmentation quality and transcript mapping. The main contribution of this paper is that we jointly find the best segmentation and transcript mapping that allow to align the image with the whole ground-truth text. The evaluation is carried out on fully annotated public databases. Furthermore, we retrieved training material with this system for the Maurdor evaluation, where the data was only annotated at the paragraph level. With the automatically segmented and annotated lines, we record a relative improvement in Word Error Rate of 35.6%.