51 research outputs found
Character Segmentation in Asian Collector's Seal Imprints: An Attempt to Retrieval Based on Ancient Character Typeface
Collector's seals provide important clues about the ownership of a book. They
contain much information pertaining to the essential elements of ancient
materials and also show the details of possession, its relation to the book,
the identity of the collectors and their social status and wealth, amongst
others. Asian collectors have typically used artistic ancient characters rather
than modern ones to make their seals. In addition to the owner's name, several
other words are used to express more profound meanings. A system that
automatically recognizes these characters can help enthusiasts and
professionals better understand the background information of these seals.
However, there is a lack of training data and labelled images, as samples of
some seals are scarce and most of them are degraded images. It is necessary to
find new ways to make full use of such scarce data. While these data are
available online, they do not contain information on the characters'position.
The goal of this research is to provide retrieval tools assist in obtaining
more information from Asian collector's seals imprints without consuming a lot
of computational resources. In this paper, a character segmentation method is
proposed to predict the candidate characters'area without any labelled training
data that contain character coordinate information. A retrieval-based
recognition system that focuses on a single character is also proposed to
support seal retrieval and matching. The experimental results demonstrate that
the proposed character segmentation method performs well on Asian collector's
seals, with 92% of the test data being correctly segmented
An Open Source Testing Tool for Evaluating Handwriting Input Methods
This paper presents an open source tool for testing the recognition accuracy
of Chinese handwriting input methods. The tool consists of two modules, namely
the PC and Android mobile client. The PC client reads handwritten samples in
the computer, and transfers them individually to the Android client in
accordance with the socket communication protocol. After the Android client
receives the data, it simulates the handwriting on screen of client device, and
triggers the corresponding handwriting recognition method. The recognition
accuracy is recorded by the Android client. We present the design principles
and describe the implementation of the test platform. We construct several test
datasets for evaluating different handwriting recognition systems, and conduct
an objective and comprehensive test using six Chinese handwriting input methods
with five datasets. The test results for the recognition accuracy are then
compared and analyzed.Comment: 5 pages, 3 figures, 11 tables. Accepted to appear at ICDAR 201
Sparse 3D convolutional neural networks
We have implemented a convolutional neural network designed for processing
sparse three-dimensional input data. The world we live in is three dimensional
so there are a large number of potential applications including 3D object
recognition and analysis of space-time objects. In the quest for efficiency, we
experiment with CNNs on the 2D triangular-lattice and 3D tetrahedral-lattice.Comment: BMVC 201
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