Abstract

Automatic identification of the author of a document has a variety of applications for both online and offline handwritten data such as facilitating the use of write dependent recognizers, verification of claimed identity for security, enabling personalized HCI and countering repudiations for legal purposes. Most of the existing writer identification techniques require the data to be from a specific text or a recognizer be available, which is not always feasible. Text-independent approaches often require large amount of data to be confident of good results. In this work, we propose a text-independent writer identification framework that uses a specified set of primitives of online handwritten data to ascertain the identity of the writer. The framework allows us to learn the properties of the script and the writers simultaneously and hence can be used with multiple languages or scripts. We demonstrate the applicability of our framework by choosing shapes of curves as primitives and show results on five different scripts and on different data sets.

Abstract

We present a document understanding system in which the arrangement of lines of text and block separators within a document are modeled by stochastic context free gram-mars. A grammar corresponds to a document genre; our system may be adapted to a new genre simply by replacing the input grammar. The system incorporates an optical character recognition system that outputs characters,their positions and font sizes. These features are combined to form a document representation of lines of text and separators. Lines of text are labeled as tokens using regular expression matching. The maximum likelihood parse of this stream of tokens and separators yields a functional labeling of the document lines. We describe business card and business letter applications.

Abstract

A hierarchical framework for document segmentation is proposed as an optimization problem. The model incorporates the dependencies between various levels of the hierarchy unlike traditional document segmentation algorithms.This framework is applied to learn the parameters of the document segmentation algorithm using optimization methods like gradient descent and Q-learning.The novelty of our approach lies in learning the segmentation parameters in the absence of ground truth.

Abstract

Sketch matching algorithms are commonly used for indexing and retrieval of documents based on printed or hand-drawn sketches. One could use a hand-held computer to do sketch-based queries to a database containing hand-drawn and printed sketches. We present an on-line hand-drawn sketch matching algorithm based on a line-based representation of sketches. A distance measure is defined for comparing two sketches based on this representation. The algorithm is computationally efficient and achieves a recall rate of 88.44% at the same precision, when tested on a database of 150 sketches collected from 5 users.