Sm. Choi et Is. Oh, A segmentation-free recognition of handwritten touching numeral pairs using modular neural network, INT J PATT, 15(6), 2001, pp. 949-966
Citations number
20
Categorie Soggetti
AI Robotics and Automatic Control
Journal title
INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE
The conventional approach to the recognition of handwritten touching numera
l pairs uses a process with two steps; splitting the touching numerals and
recognizing individual numerals. It shows a limitation mainly due to a larg
e variation in touching styles between two numerals. In this paper, we adop
t the segmentation-free approach, which regards a touching numeral pair as
an atomic pattern. Two important issues are raised, i.e. solving the large-
set classification and constructing a large-size training set. For the 100-
class classification, we use a modular neural network which consists of 100
separate subnetworks. We construct the training set with a balance among 1
00 classes and using a sufficient amount by extracting actual samples from
a numeral database and synthesizing samples with a scheme of forcing two nu
merals to touch. The experimental results show a promising performance.