Abstract book (pdf) - ICPR 2010

and video frames were used to evaluate the performance of the proposed approach. Experimental results demonstrated the
validity of our approach.
13:50-14:10, Paper ThBT6.2
Scene Text Extraction with Edge Constraint and Text Collinearity
Lee, Seonghun, KAIST
Cho, MinSu, KAIST
Jung, Kyomin, KAIST
Kim, Jin Hyung, KAIST
In this paper, we propose a framework for isolating text regions from natural scene images. The main algorithm has two
functions: it generates text region candidates, and it verifies of the label of the candidates (text or non-text). The text region
candidates are generated through a modified K-means clustering algorithm, which references texture features, edge information
and color information. The candidate labels are then verified in a global sense by the Markov Random Field model
where collinearity weight is added as long as most texts are aligned. The proposed method achieves reasonable accuracy
for text extraction from moderately difficult examples from the ICDAR 2003 database.
14:10-14:30, Paper ThBT6.3
Typographical Features for Scene Text Recognition
Weinman, Jerod, Grinnell Coll.
Scene text images feature an abundance of font style variety but a dearth of data in any given query. Recognition methods
must be robust to this variety or adapt to the query data’s characteristics. To achieve this, we augment a semi-Markov
model—-integrating character segmentation and recognition—-with a bigram model of character widths. Softly promoting
segmentations that exhibit font metrics consistent with those learned from examples, we use the limited information available
while avoiding error-prone direct estimates and hard constraints. Incorporating character width bigrams in this fashion
improves recognition on low-resolution images of signs containing text in many fonts.
14:30-14:50, Paper ThBT6.4
A Visual Attention based Approach to Text Extraction
Sun, Qiaoyu, HuaihaiInstitute of Tech.
Lu, Yue, East China Normal Univ.
Sun, Shiliang, East China Normal Univ.
A visual attention based approach is proposed to extract texts from complicated background in camera-based images.
First, it applies the simplified visual attention model to highlight the region of interest (ROI) in an input image and to
yield a map, named the VA map, consisting of the ROIs. Second, an edge map of image containing the edge information
of four directions is obtained by Sobel operators. Character areas are detected by connected component analysis and
merged into candidate text regions. Finally, the VA map is employed to confirm the candidate text regions. The experimental
results demonstrate that the proposed method can effectively extract text information and locate text regions contained in
camera-based images. It is robust not only for font, size, color, language, space, alignment and complexity of background,
but also for perspective distortion and skewed texts embedded in images.
14:50-15:10, Paper ThBT6.5
New Wavelet and Color Features for Text Detection in Video
Palaiahnakote, Shivakumara, National Univ. of Singapore
Phan, Trung Quy, National Univ. of Singapore
Tan, Chew-Lim, National Univ. of Singapore
Automatic text detection in video is an important task for efficient and accurate indexing and retrieval of multimedia data
such as events identification, events boundary identification etc. This paper presents a new method comprising of wavelet
decomposition and color features namely R, G and B. The wavelet decomposition is applied on three color bands separately
to obtain three high frequency sub bands (LH, HL and HH) and then the average of the three sub bands for each color
band is computed further to enhance the text pixels in video frame. To take advantage of wavelet and color information,
we again take the average of the three average images (AoA) obtained by the former step to increase the gap between text
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