120 research outputs found
A New Approach to Keyphrase Extraction Using Neural Networks
Keyphrases provide a simple way of describing a document, giving the reader
some clues about its contents. Keyphrases can be useful in a various
applications such as retrieval engines, browsing interfaces, thesaurus
construction, text mining etc.. There are also other tasks for which keyphrases
are useful, as we discuss in this paper. This paper describes a neural network
based approach to keyphrase extraction from scientific articles. Our results
show that the proposed method performs better than some state-of-the art
keyphrase extraction approaches.Comment: International Journal of Computer Science Issues online at
http://ijcsi.org/articles/A-New-Approach-to-Keyphrase-Extraction-Using-Neural-Networks.ph
Text/Graphics Separation and Skew Correction of Text Regions of Business Card Images for Mobile Devices
Separation of the text regions from background texture and graphics is an
important step of any optical character recognition system for the images
containing both texts and graphics. In this paper, we have presented a novel
text/graphics separation technique and a method for skew correction of text
regions extracted from business card images captured with a cell-phone camera.
At first, the background is eliminated at a coarse level based on intensity
variance. This makes the foreground components distinct from each other. Then
the non-text components are removed using various characteristic features of
text and graphics. Finally, the text regions are skew corrected for further
processing. Experimenting with business card images of various resolutions, we
have found an optimum performance of 98.25% (recall) with 0.75 MP images, that
takes 0.17 seconds processing time and 1.1 MB peak memory on a moderately
powerful computer (DualCore 1.73 GHz Processor, 1 GB RAM, 1 MB L2 Cache). The
developed technique is computationally efficient and consumes low memory so as
to be applicable on mobile devices
Handwritten Isolated Bangla Compound Character Recognition: a new benchmark using a novel deep learning approach
In this work, a novel deep learning technique for the recognition of
handwritten Bangla isolated compound character is presented and a new benchmark
of recognition accuracy on the CMATERdb 3.1.3.3 dataset is reported. Greedy
layer wise training of Deep Neural Network has helped to make significant
strides in various pattern recognition problems. We employ layerwise training
to Deep Convolutional Neural Networks (DCNN) in a supervised fashion and
augment the training process with the RMSProp algorithm to achieve faster
convergence. We compare results with those obtained from standard shallow
learning methods with predefined features, as well as standard DCNNs.
Supervised layerwise trained DCNNs are found to outperform standard shallow
learning models such as Support Vector Machines as well as regular DCNNs of
similar architecture by achieving error rate of 9.67% thereby setting a new
benchmark on the CMATERdb 3.1.3.3 with recognition accuracy of 90.33%,
representing an improvement of nearly 10%
A Two Stage Classification Approach for Handwritten Devanagari Characters
The paper presents a two stage classification approach for handwritten
devanagari characters The first stage is using structural properties like
shirorekha, spine in character and second stage exploits some intersection
features of characters which are fed to a feedforward neural network. Simple
histogram based method does not work for finding shirorekha, vertical bar
(Spine) in handwritten devnagari characters. So we designed a differential
distance based technique to find a near straight line for shirorekha and spine.
This approach has been tested for 50000 samples and we got 89.12% succes
Classification Of Gradient Change Features Using MLP For Handwritten Character Recognition
A novel, generic scheme for off-line handwritten English alphabets character
images is proposed. The advantage of the technique is that it can be applied in
a generic manner to different applications and is expected to perform better in
uncertain and noisy environments. The recognition scheme is using a multilayer
perceptron(MLP) neural networks. The system was trained and tested on a
database of 300 samples of handwritten characters. For improved generalization
and to avoid overtraining, the whole available dataset has been divided into
two subsets: training set and test set. We achieved 99.10% and 94.15% correct
recognition rates on training and test sets respectively. The purposed scheme
is robust with respect to various writing styles and size as well as presence
of considerable noise
A Novel Approach in detecting pose orientation of a 3D face required for face
In this paper we present a novel approach that takes as input a 3D image and
gives as output its pose i.e. it tells whether the face is oriented with
respect the X, Y or Z axes with angles of rotation up to 40 degree. All the
experiments have been performed on the FRAV3D Database. After applying the
proposed algorithm to the 3D facial surface we have obtained i.e. on 848 3D
face images our method detected the pose correctly for 566 face images,thus
giving an approximately 67 % of correct pose detection
A Hough Transform based Technique for Text Segmentation
Text segmentation is an inherent part of an OCR system irrespective of the
domain of application of it. The OCR system contains a segmentation module
where the text lines, words and ultimately the characters must be segmented
properly for its successful recognition. The present work implements a Hough
transform based technique for line and word segmentation from digitized images.
The proposed technique is applied not only on the document image dataset but
also on dataset for business card reader system and license plate recognition
system. For standardization of the performance of the system the technique is
also applied on public domain dataset published in the website by CMATER,
Jadavpur University. The document images consist of multi-script printed and
hand written text lines with variety in script and line spacing in single
document image. The technique performs quite satisfactorily when applied on
mobile camera captured business card images with low resolution. The usefulness
of the technique is verified by applying it in a commercial project for
localization of license plate of vehicles from surveillance camera images by
the process of segmentation itself. The accuracy of the technique for word
segmentation, as verified experimentally, is 85.7% for document images, 94.6%
for business card images and 88% for surveillance camera images
A novel approach for nose tip detection using smoothing by weighted median filtering applied to 3D face images in variant poses
This paper is based on an application of smoothing of 3D face images followed
by feature detection i.e. detecting the nose tip. The present method uses a
weighted mesh median filtering technique for smoothing. In this present
smoothing technique we have built the neighborhood surrounding a particular
point in 3D face and replaced that with the weighted value of the surrounding
points in 3D face image. After applying the smoothing technique to the 3D face
images our experimental results show that we have obtained considerable
improvement as compared to the algorithm without smoothing. We have used here
the maximum intensity algorithm for detecting the nose-tip and this method
correctly detects the nose-tip in case of any pose i.e. along X, Y, and Z axes.
The present technique gave us worked successfully on 535 out of 542 3D face
images as compared to the method without smoothing which worked only on 521 3D
face images out of 542 face images. Thus we have obtained a 98.70% performance
rate over 96.12% performance rate of the algorithm without smoothing. All the
experiments have been performed on the FRAV3D database.Comment: 6 page
Automatic White Blood Cell Measuring Aid for Medical Diagnosis
Blood related invasive pathological investigations play a major role in
diagnosis of diseases. But in India and other third world countries there are
no enough pathological infrastructures for medical diagnosis. Moreover, most of
the remote places of those countries have neither pathologists nor physicians.
Telemedicine partially solves the lack of physicians. But the pathological
investigation infrastructure can not be integrated with the telemedicine
technology. The objective of this work is to automate the blood related
pathological investigation process. Detection of different white blood cells
has been automated in this work. This system can be deployed in the remote area
as a supporting aid for telemedicine technology and only high school education
is sufficient to operate it. The proposed system achieved 97.33 percent
accuracy for the samples collected to test this system.Comment: 6 pages, International Conferenc
Detection of pose orientation across single and multiple axes in case of 3D face images
In this paper, we propose a new approach that takes as input a 3D face image
across X, Y and Z axes as well as both Y and X axes and gives output as its
pose i.e. it tells whether the face is oriented with respect the X, Y or Z axes
or is it oriented across multiple axes with angles of rotation up to 42 degree.
All the experiments have been performed on the FRAV3D, GAVADB and Bosphorus
database which has two figures of each individual across multiple axes. After
applying the proposed algorithm to the 3D facial surface from FRAV3D on 848 3D
faces, 566 3D faces were correctly recognized for pose thus giving 67% of
correct identification rate. We had experimented on 420 images from the GAVADB
database, and only 336 images were detected for correct pose identification
rate i.e. 80% and from Bosphorus database on 560 images only 448 images were
detected for correct pose identification i.e. 80%.abstract goes here.Comment: 12 page
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