Abstract book (pdf) - ICPR 2010

13:30-16:30, Paper TuBCT9.7
Scribe Identification in Medieval English Manuscripts
Gilliam, Tara, Univ. of York
Wilson, Richard, Univ. of York
Clark, John A., Univ. of York
In this paper we present work on automated scribe identification on a new Middle-English manuscript dataset from around
the 14 th – 15 th century. We discuss the image and textual problems encountered in processing historical documents, and
demonstrate the effect of accounting for manuscript style on the writer identification rate. The grapheme codebook method
is used to achieve a Top-1 classification accuracy of up to 77% with a modification to the distance measure. The performance
of the Sparse Multinomial Logistic Regression classifier is compared against five k-nn classifiers. We also consider
classification against the principal components and propose a method for visualising the principal component vectors in
terms of the original grapheme features.
13:30-16:30, Paper TuBCT9.8
Recognition of Handwritten Arabic (Indian) Numerals using Freeman’s Chain Codes and Abductive Network Classifier
Lawal, Isah Abdullahi, King Fahd Univ. of Petroleum & Minerals
Abdel-Aal, Radwan E., King Fahd Univ. of Petroleum & Minerals
Mahmoud, Sabri A., King Fahd Univ. of Petroleum & Minerals
Accurate automatic recognition of handwritten Arabic numerals has several important applications, e.g. in banking transactions,
automation of postal services, and other data entry related applications. A number of modelling and machine learning
techniques have been used for handwritten Arabic numerals recognition, including Neural Network, Support Vector
Machine, and Hidden Markov Models. This paper proposes the use of abductive networks to the problem. We studied the
performance of abductive network architecture on a dataset of 21120 samples of handwritten 0-9 digits produced by 44
writers. We developed a new feature set using histograms of contour points chain codes. Recognition rates as high as
99.03% were achieved, which surpass the performance reported in the literature for other recognition techniques on the
same data set. Moreover, the technique achieves a significant reduction in the number of features required.
13:30-16:30, Paper TuBCT9.9
A SVM-HMM based Online Classifier for Handwritten Chemical Symbols
Zhang, Yang, Nankai Univ.
Shi, Guangshun, Nankai Univ.
Wang, Kai, Nankai Univ.
This paper presents a novel double-stage classifier for handwritten chemical symbols recognition task. The first stage is
rough classification, SVM method is used to distinguish non-ring structure (NRS) and organic ring structure (ORS) symbols,
while HMM method is used for fine recognition at second stage. A point-sequence-reordering algorithm is proposed
to improve the recognition accuracy of ORS symbols. Our test data set contains 101 chemical symbols, 9090 training
samples and 3232 test samples. Finally, we obtained top-1 accuracy of 93.10% and top-3 accuracy of 98.08% based on
the test data set.
13:30-16:30,Paper TuBCT9.10
Symbol Recognition Combining Vectorial and Pixel-Level Features for Line Drawings
Su, Feng, Nanjing Univ.
Lu, Tong, Nanjing Univ.
Yang, Ruoyu, Nanjing Univ.
In this paper, we present an approach for symbol representation and recognition in line drawings, integrating both the vector-based
structural description and pixel-level statistical features of the symbol. For the former, a vectorial template is
defined on the basis of the vectorization model and exploited in segmenting symbols from the line network. For the latter,
a Radon-transform-based signature is employed to characterize shapes on the symbol and the components level. Experimental
results on real technical drawings are presented to show the promising aspect of our approach.
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