By Topic

Redundant Bit Vectors for Robust Indexing and Retrieval of Electronic Ink

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
K. Chellapilla ; Microsoft Resear ; J. Platt

This paper presents a redundant bit vector approach for indexing and retrieval of handwritten words captured using an electronic pen or tablet. Handwritten words (cursive or print) are first segmented into strokes and each stroke is featurized using a neural network. Oriented principal component analysis (OPCA) is used for dimensionality reduction while ensuring robustness to handwriting variation (noise). Redundant bit vectors are used to index the resulting low dimensional representations for efficient storage and retrieval. Experimental results on large datasets with 898,652 handwritten words show good retrieval performance that is robust to handwriting variations and generalizes well over different writers and writing styles.

Published in:

Ninth International Conference on Document Analysis and Recognition (ICDAR 2007)  (Volume:1 )

Date of Conference:

23-26 Sept. 2007