Abstract book (pdf) - ICPR 2010

Automatic identification of an individual based on his/her handwriting characteristics is an important forensic tool. In a
computational forensic scenario, presence of huge amount of text/information in a questioned document cannot be always
ensured. Also, compromising in terms of systems reliability under such situation is not desirable. We here propose a system
to encounter such adverse situation in the context of Bengali script. Experiments with discrete directional feature and gradient
feature are reported here, along with Support Vector Machine (SVM) as classifier. We got promising results of 95.19%
writer identification accuracy at first top choice and 99.03% when considering first three top choices.
13:30-16:30,Paper TuBCT9.40
Document Image Retrieval using Feature Combination in Kernel Space
Hassan, Ehtesham, Indian Inst. of Tech. Delhi
Chaudhury, Santanu, Indian Inst. of Tech. Delhi
Gopal, M, Indian Inst. of Tech. Delhi
The paper presents application of multiple features for word based document image indexing and retrieval. A novel framework
to perform Multiple Kernel Learning for indexing using the Kernel based Distance Based Hashing is proposed. The
Genetic Algorithm based framework is used for optimization. Two different features representing the structural organization
of word shape are defined. The optimal combination of both the features for indexing is learned by performing MKL. The
retrieval results for document collection belonging to Devanagari script are presented.
13:30-16:30,Paper TuBCT9.41
A Novel Handwritten Urdu Word Spotting based on Connected Components Analysis
Sagheer, Malik Waqas, CENPARMI, Concordia Univ.
Nobile, Nicola, Concordia Univ. CENPARMI
He, Chun Lei, Concordia Univ.
Suen, Ching Y.
We propose a novel word spotting system for Urdu words within handwritten text lines. Spatial information of diacritics
is integrated to the detection of the main connected components in candidate words generation. An Urdu word recognition
system is effectively designed and applied to classify the candidate words. In this word recognition system, compound
features and SVM were adapted. The verification/rejection process was based on the outputs from the Urdu word recognition
system and the image’s global features were applied to achieve a promising result. As a result, a high 92.11% correct
segmentation rate, a 50.75% word spotting precision rate were achieved while maintaining a 70.1% recall on CENPARMI’s
Urdu Database.
13:30-16:30,Paper TuBCT9.42
Computer Assisted Transcription of Text Images: Results on the GERMANA Corpus and Analysis of Improvements
Needed for Practical Use
Romero Gomez, Verónica, Univ. Pol. De Valencia
Toselli, Alejandro Héctor, Univ. Pol. De Valencia
Vidal, Enrique, Univ. Pol. De Valencia
We present a study of the application of Computer Assisted Transcription of Text Images (CATTI) to a task which is much
closer to real applications than other tasks previously studied. The new task consists in the transcription of a new publicly
available historic handwritten document, called GERMANA. A detailed analysis of the main factors influencing the system
performance are exposed and some strategies to circumvent them are proposed.
13:30-16:30,Paper TuBCT9.43
OCR Post-Processing using Weighted Finite-State Transducers
Llobet, Rafael, Univ. Pol. De Valencia
Navarro Cerdán, José Ramón, Univ. Pol. De Valencia
Perez-Cortes, Juan-Carlos, Univ. Pol. De Valencia
Arlandis, Joaquim, Univ. Pol. De Valencia
A new approach for Stochastic Error-Correcting Language Modeling based on Weighted Finite-State Transducers (WFSTs)
is proposed as a method to post-process the results of an optical character recognizer (OCR). Instead of using the recognized
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