1,600 research outputs found
Handwritten Character Recognition of South Indian Scripts: A Review
Handwritten character recognition is always a frontier area of research in
the field of pattern recognition and image processing and there is a large
demand for OCR on hand written documents. Even though, sufficient studies have
performed in foreign scripts like Chinese, Japanese and Arabic characters, only
a very few work can be traced for handwritten character recognition of Indian
scripts especially for the South Indian scripts. This paper provides an
overview of offline handwritten character recognition in South Indian Scripts,
namely Malayalam, Tamil, Kannada and Telungu.Comment: Paper presented on the "National Conference on Indian Language
Computing", Kochi, February 19-20, 2011. 6 pages, 5 figure
DTW-Radon-based Shape Descriptor for Pattern Recognition
International audienceIn this paper, we present a pattern recognition method that uses dynamic programming (DP) for the alignment of Radon features. The key characteristic of the method is to use dynamic time warping (DTW) to match corresponding pairs of the Radon features for all possible projections. Thanks to DTW, we avoid compressing the feature matrix into a single vector which would otherwise miss information. To reduce the possible number of matchings, we rely on a initial normalisation based on the pattern orientation. A comprehensive study is made using major state-of-the-art shape descriptors over several public datasets of shapes such as graphical symbols (both printed and hand-drawn), handwritten characters and footwear prints. In all tests, the method proves its generic behaviour by providing better recognition performance. Overall, we validate that our method is robust to deformed shape due to distortion, degradation and occlusion
Off-line Arabic Handwriting Recognition System Using Fast Wavelet Transform
In this research, off-line handwriting recognition system for Arabic alphabet is
introduced. The system contains three main stages: preprocessing, segmentation and
recognition stage. In the preprocessing stage, Radon transform was used in the design
of algorithms for page, line and word skew correction as well as for word slant
correction. In the segmentation stage, Hough transform approach was used for line
extraction. For line to words and word to characters segmentation, a statistical method
using mathematic representation of the lines and words binary image was used.
Unlike most of current handwriting recognition system, our system simulates the
human mechanism for image recognition, where images are encoded and saved in
memory as groups according to their similarity to each other. Characters are
decomposed into a coefficient vectors, using fast wavelet transform, then, vectors,
that represent a character in different possible shapes, are saved as groups with one
representative for each group. The recognition is achieved by comparing a vector of
the character to be recognized with group representatives.
Experiments showed that the proposed system is able to achieve the recognition task
with 90.26% of accuracy. The system needs only 3.41 seconds a most to recognize a
single character in a text of 15 lines where each line has 10 words on average
Data comparison schemes for Pattern Recognition in Digital Images using Fractals
Pattern recognition in digital images is a common problem with application in
remote sensing, electron microscopy, medical imaging, seismic imaging and
astrophysics for example. Although this subject has been researched for over
twenty years there is still no general solution which can be compared with the
human cognitive system in which a pattern can be recognised subject to
arbitrary orientation and scale.
The application of Artificial Neural Networks can in principle provide a very
general solution providing suitable training schemes are implemented.
However, this approach raises some major issues in practice. First, the CPU
time required to train an ANN for a grey level or colour image can be very
large especially if the object has a complex structure with no clear geometrical
features such as those that arise in remote sensing applications. Secondly,
both the core and file space memory required to represent large images and
their associated data tasks leads to a number of problems in which the use of
virtual memory is paramount.
The primary goal of this research has been to assess methods of image data
compression for pattern recognition using a range of different compression
methods. In particular, this research has resulted in the design and
implementation of a new algorithm for general pattern recognition based on
the use of fractal image compression.
This approach has for the first time allowed the pattern recognition problem to
be solved in a way that is invariant of rotation and scale. It allows both ANNs
and correlation to be used subject to appropriate pre-and post-processing
techniques for digital image processing on aspect for which a dedicated
programmer's work bench has been developed using X-Designer
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