Abstract book (pdf) - ICPR 2010

WeCT7 Dolmabahçe Hall C
Handwriting Recognition Regular Session
Session chair: Doermann, David (Univ. of Maryland)
15:40-16:00, Paper WeCT7.1
Consensus Network based Hypotheses Combination for Arabic Offline Handwriting Recognition
Prasad, Rohit, Raytheon BBN Tech.
Kamali, Matin, BBN Tech.
Belanger, David, Raytheon BBN Tech.
Rosti, Antti-Veikko, Raytheon BBN Tech.
Matsoukas, Spyros, Raytheon BBN Tech.
Natarajan, P., BBN Tech.
Offline handwriting recognition (OHR) is an extremely challenging task because of many factors including variations in
writing style, writing device and material, and noise in the scanning and collection process. Due to the diverse nature of
the above challenges, it is highly unlikely that a single recognition technique can address all the characteristics of realworld
handwritten documents. Therefore, one must consider designing different systems, each addressing specific challenges
in the handwritten corpus, and then combining the hypotheses from these diverse systems. To that end, we present
an innovative approach for combining hypotheses from multiple handwriting recognition systems. Our approach is based
on generating a consensus network using hypotheses from a diverse set of handwriting recognition systems. Next, we decode
the consensus network for producing the best possible hypothesis given an error criterion. Experimental results on
an Arabic OHR task show that our combination algorithm outperforms the NIST ROVER technique and results in a 7%
relative reduction in the word error rate over the single best OHR system.
16:00-16:20, Paper WeCT7.2
A Novel Lexicon Reduction Method for Arabic Handwriting Recognition
Wshah, Safwan, SUNY Buffalo
Govindaraju, Venu, Univ. at Buffalo
Li, Huiping, Applied Media Analysis Inc.
Cheng, Yanfen, Wuhan Univ. of Tech.
In this paper, we present a method for lexicon size reduction which can be used as an important pre-processing for an offline
Arabic word recognition. The method involves extraction of the dot descriptors and PAWs (Piece of Arabic Word ).
Then the number and position of dots and the number of the PAWs are used to eliminate unlikely candidates. The extraction
of the dot descriptors is based on defined rules followed by a convolutional neural network for verification. The reduction
algorithm makes use of the combination of two features with a dynamic matching scheme. On IFN/ENIT database of
26459 Arabic handwritten word images we achieved a reduction rate of 87% with accuracy above 93%.
16:20-16:40, Paper WeCT7.3
A Novel Verification System for Handwritten Words Recognition
Guichard, Laurent, IRISA - INRIA
Toselli, Alejandro Héctor, Univ. Pol. de Valencia
Couasnon, Bertrand, Irisa / Insa
In the field of isolated handwritten word recognition, the development of highly effective verification systems to reject
words presenting ambiguities is still an active research topic. In this paper, a novel verification system based on support
vector machine scoring and multiple reject class-dependent thresholds is presented. In essence, a set of support vector machines
appended to a standard HMM-based recognition system provides class-dependent confidence measures employed
by the verification mechanism to accept or reject the recognized hypotheses. Experimental results on RIMES database
show that this approach outperforms other state-of-the-art approaches.
16:40-17:00, Paper WeCT7.4
Multi-Template GAT/PAT Correlation for Character Recognition with a Limited Quantity of Data
Wakahara, Toru, Hosei Univ.
Yamashita, Yukihiko, Tokyo Inst. of Tech.
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