Text-image alignment for historical handwritten documents

S. Zinger; J. Nerbonne; L. Schomaker

doi:10.1117/12.805511

19 January 2009 Text-image alignment for historical handwritten documents

S. Zinger, J. Nerbonne, L. Schomaker

Proceedings Volume 7247, Document Recognition and Retrieval XVI; 724703 (2009) https://doi.org/10.1117/12.805511
Event: IS&T/SPIE Electronic Imaging, 2009, San Jose, California, United States

Abstract

We describe our work on text-image alignment in context of building a historical document retrieval system. We aim at aligning images of words in handwritten lines with their text transcriptions. The images of handwritten lines are automatically segmented from the scanned pages of historical documents and then manually transcribed. To train automatic routines to detect words in an image of handwritten text, we need a training set - images of words with their transcriptions. We present our results on aligning words from the images of handwritten lines and their corresponding text transcriptions. Alignment based on the longest spaces between portions of handwriting is a baseline. We then show that relative lengths, i.e. proportions of words in their lines, can be used to improve the alignment results considerably. To take into account the relative word length, we define the expressions for the cost function that has to be minimized for aligning text words with their images. We apply right to left alignment as well as alignment based on exhaustive search. The quality assessment of these alignments shows correct results for 69% of words from 100 lines, or 90% of partially correct and correct alignments combined.

Citation Download Citation

S. Zinger, J. Nerbonne, and L. Schomaker "Text-image alignment for historical handwritten documents", Proc. SPIE 7247, Document Recognition and Retrieval XVI, 724703 (19 January 2009); https://doi.org/10.1117/12.805511

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available