Abstract book (pdf) - ICPR 2010

didate word of the document with which the query image is to be searched. Based on the similarity score, we retrieve the
document where the query image is found. Experimental results on Bangla, Devnagari and Gurumukhi scripts document
image databases confirm the feasibility and efficiency of our proposed approach.
13:30-16:30,Paper TuBCT9.36
Stochastic Segment Model Adaptation for Offline Handwriting Recognition
Prasad, Rohit, Raytheon BBN Tech.
Bhardwaj, Anurag, SUNY Buffalo
Subramanian, Krishna, Raytheon BBN Tech.
Cao, Huaigu, Raytheon BBN Tech.
Natarajan, P., BBN Tech.
In this paper, we present techniques for unsupervised adaptation of stochastic segment models to improve accuracy on
large vocabulary offline handwriting recognition (OHR) tasks. We build upon our previous work on stochastic segment
modeling for Arabic OHR. In our previous work, stochastic character segments for each n-best hypothesis were generated
by a hidden Markov model (HMM) recognizer, and then a segmental model was used as an additional knowledge source
for re-ranking the n-best list. Here, we describe a novel framework for unsupervised adaptation. It integrates both HMM
and segment model adaptation to achieve significant gains over un-adapted recognition. Experimental results demonstrate
the efficacy of our proposed method on a large corpus of handwritten Arabic documents.
13:30-16:30,Paper TuBCT9.37
Shape-Based Image Retrieval using a New Descriptor based on the Radon and Wavelet Transforms
Nacereddine, Nafaa, LORIA
Tabbone, Salvatore, Univ. Nancy 2-LORIA
Ziou, Djemel, Sherbrooke Univ.
Hamami, Latifa, Ec. Nationale Pol.
In this paper, the Radon transform is used to design a new descriptor called Phi-signature invariant to usual geometric
transformations. Experiments show the effectiveness of the multilevel representation of the descriptor built from Phi-signature
and R.
13:30-16:30,Paper TuBCT9.38
CUDA Implementation of Deformable Pattern Recognition and its Application to MNIST Handwritten Digit Database
Mizukami, Yoshiki, Yamaguchi Univ.
Tadamura, Katsumi, Yamaguchi Univ.
Warrell, Jonathan, Oxford Brookes University
Li, Peng, Univ. Coll. London
Prince, Simon, Univ. Coll. London
In this study we propose a deformable pattern recognition method with CUDA implementation. In order to achieve the
proper correspondence between foreground pixels of input and prototype images, a pair of distance maps are generated
from input and prototype images, whose pixel values are given based on the distance to the nearest foreground pixel. Then
a regularization technique computes the horizontal and vertical displacements based on these distance maps. The dissimilarity
is measured based on the eight-directional derivative of input and prototype images in order to leverage characteristic
information on the curvature of line segments that might be lost after the deformation. The prototype-parallel displacement
computation on CUDA and the gradual prototype elimination technique are employed for reducing the computational time
without sacrificing the accuracy. A simulation shows that the proposed method with the k-nearest neighbor classifier gives
the error rate of 0.57% for the MNIST handwritten digit database.
13:30-16:30,Paper TuBCT9.39
Text Independent Writer Identification for Bengali Script
Chanda, Sukalpa, GJØVIK Univ. Coll.
Franke, Katrin, Gjøvik Univ. Coll.
Pal, Umapada, Indian Statistical Inst.
Wakabayashi, Tetsushi, Mie Univ.
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