Dense correspondences and ancient texts

Tal Hassner*, Lior Wolf, Nachum Dershowitz, Gil Sadeh2, Daniel Stökl Ben-Ezra

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

1 Scopus citations


This chapter concerns applications of dense correspondences to images of a very different nature than those considered in previous chapters. Rather than images of natural or man-made scenes and objects, here, we deal with images of texts. We present a novel, dense correspondence-based approach to text image analysis instead of the more traditional approach of analysis at the character level (e.g., existing optical character recognition methods) or word level (the so called word spotting approach). We focus on the challenging domain of historical text image analysis. Such texts are handwritten and are often severely corrupted by noise and degradation, making them difficult to handle with existing methods. Our system is designed for the particular task of aligning such manuscript images to their transcripts. Our proposed alternative to performing this task manually is a system which directly matches the historical text image with a synthetic image rendered from the transcript. These matches are performed at the pixel level, by using SIFT flow applied to a novel per pixel representation. Our pipeline is robust to document degradation, variations between script styles and nonlinear image transformations.

Original languageEnglish
Title of host publicationDense Image Correspondences for Computer Vision
PublisherSpringer International Publishing
Number of pages17
ISBN (Electronic)9783319230481
ISBN (Print)9783319230474
StatePublished - 1 Jan 2015


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