1,179 research outputs found
CNN training with graph-based sample preselection: application to handwritten character recognition
In this paper, we present a study on sample preselection in large training
data set for CNN-based classification. To do so, we structure the input data
set in a network representation, namely the Relative Neighbourhood Graph, and
then extract some vectors of interest. The proposed preselection method is
evaluated in the context of handwritten character recognition, by using two
data sets, up to several hundred thousands of images. It is shown that the
graph-based preselection can reduce the training data set without degrading the
recognition accuracy of a non pretrained CNN shallow model.Comment: Paper of 10 pages. Minor spelling corrections brought regarding the
v2. Accepted as an oral paper in the 13th IAPR Internationale Workshop on
Document Analysis Systems (DAS 2018
Open-Category Classification by Adversarial Sample Generation
In real-world classification tasks, it is difficult to collect training
samples from all possible categories of the environment. Therefore, when an
instance of an unseen class appears in the prediction stage, a robust
classifier should be able to tell that it is from an unseen class, instead of
classifying it to be any known category. In this paper, adopting the idea of
adversarial learning, we propose the ASG framework for open-category
classification. ASG generates positive and negative samples of seen categories
in the unsupervised manner via an adversarial learning strategy. With the
generated samples, ASG then learns to tell seen from unseen in the supervised
manner. Experiments performed on several datasets show the effectiveness of
ASG.Comment: Published in IJCAI 201
Machine Learning for Handwriting Recognition
With the knowledge of current data about particular subject, machine learning tries to extract hidden information that lies in the data. By applying some mathematical functions and concepts to extract hidden information, machine learning can be achieved and we can predict output for unknown data. Pattern recognition is one of the main application of ML. Patterns are usually recognized with the help of large image data-set. Handwriting recognition is an application of pattern recognition through image. By using such concepts, we can train computers to read letters and numbers belonging to any language present in an image. There exists several methods by which we can recognize hand-written characters. We will be discussing some of the methods in this paper
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