2,307 research outputs found
A review on handwritten character and numeral recognition for Roman, Arabic, Chinese and Indian scripts
There are a lot of intensive researches on handwritten character recognition
(HCR) for almost past four decades. The research has been done on some of
popular scripts such as Roman, Arabic, Chinese and Indian. In this paper we
present a review on HCR work on the four popular scripts. We have summarized
most of the published paper from 2005 to recent and also analyzed the various
methods in creating a robust HCR system. We also added some future direction of
research on HCR.Comment: 8 page
Similar Handwritten Chinese Character Discrimination by Weakly Supervised Learning
Traditional approaches for handwritten Chinese character recognition suffer
in classifying similar characters. In this paper, we propose to discriminate
similar handwritten Chinese characters by using weakly supervised learning. Our
approach learns a discriminative SVM for each similar pair which simultaneously
localizes the discriminative region of similar character and makes the
classification. For the first time, similar handwritten Chinese character
recognition (SHCCR) is formulated as an optimization problem extended from SVM.
We also propose a novel feature descriptor, Gradient Context, and apply
bag-of-words model to represent regions with different scales. In our method,
we do not need to select a sized-fixed sub-window to differentiate similar
characters. The unconstrained property makes our method well adapted to high
variance in the size and position of discriminative regions in similar
handwritten Chinese characters. We evaluate our proposed approach over the
CASIA Chinese character data set and the results show that our method
outperforms the state of the art.Comment: 5 figures, 8 page
End to End Recognition System for Recognizing Offline Unconstrained Vietnamese Handwriting
Inspired by recent successes in neural machine translation and image caption
generation, we present an attention based encoder decoder model (AED) to
recognize Vietnamese Handwritten Text. The model composes of two parts: a
DenseNet for extracting invariant features, and a Long Short-Term Memory
network (LSTM) with an attention model incorporated for generating output text
(LSTM decoder), which are connected from the CNN part to the attention model.
The input of the CNN part is a handwritten text image and the target of the
LSTM decoder is the corresponding text of the input image. Our model is trained
end-to-end to predict the text from a given input image since all the parts are
differential components. In the experiment section, we evaluate our proposed
AED model on the VNOnDB-Word and VNOnDB-Line datasets to verify its efficiency.
The experiential results show that our model achieves 12.30% of word error rate
without using any language model. This result is competitive with the
handwriting recognition system provided by Google in the Vietnamese Online
Handwritten Text Recognition competition
Handwritten Character Recognition In Malayalam Scripts- A Review
Handwritten character recognition is one of the most challenging and ongoing
areas of research in the field of pattern recognition. HCR research is matured
for foreign languages like Chinese and Japanese but the problem is much more
complex for Indian languages. The problem becomes even more complicated for
South Indian languages due to its large character set and the presence of
vowels modifiers and compound characters. This paper provides an overview of
important contributions and advances in offline as well as online handwritten
character recognition of Malayalam scripts.Comment: 11 pages,4 figures,2 table
Classifier Fusion Method to Recognize Handwritten Kannada Numerals
Optical Character Recognition (OCR) is one of the important fields in image
processing and pattern recognition domain. Handwritten character recognition
has always been a challenging task. Only a little work can be traced towards
the recognition of handwritten characters for the south Indian languages.
Kannada is one such south Indian language which is also one of the official
language of India. Accurate recognition of Kannada characters is a challenging
task because of the high degree of similarity between the characters. Hence,
good quality features are to be extracted and better classifiers are needed to
improve the accuracy of the OCR for Kannada characters. This paper explores the
effectiveness of feature extraction method like run length count (RLC) and
directional chain code (DCC) for the recognition of handwritten Kannada
numerals. In this paper, a classifier fusion method is implemented to improve
the recognition rate. For the classifier fusion, we have considered K-nearest
neighbour (KNN) and Linear classifier (LC). The novelty of this method is to
achieve better accuracy with few features using classifier fusion approach.
Proposed method achieves an average recognition rate of 96%.Comment: 6 pages having 3 tables and 9 figures. Published in ICECT 2012
conferenc
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
Handwritten character recognition using some (anti)-diagonal structural features
In this paper, we present a methodology for off-line handwritten character
recognition. The proposed methodology relies on a new feature extraction
technique based on structural characteristics, histograms and profiles. As
novelty, we propose the extraction of new eight histograms and four profiles
from the matrices that represent the characters, creating
256-dimension feature vectors. These feature vectors are then employed in a
classification step that uses a -means algorithm. We performed experiments
using the NIST database to evaluate our proposal. Namely, the recognition
system was trained using 1000 samples and 64 classes for each symbol and was
tested on 500 samples for each symbol. We obtain promising accuracy results
that vary from 81.74\% to 93.75\%, depending on the difficulty of the character
category, showing better accuracy results than other methods from the state of
the art also based on structural characteristics.Comment: Revised version with a number of improvements and update references,
9 page
Neural Computing for Online Arabic Handwriting Character Recognition using Hard Stroke Features Mining
Online Arabic cursive character recognition is still a big challenge due to
the existing complexities including Arabic cursive script styles, writing
speed, writer mood and so forth. Due to these unavoidable constraints, the
accuracy of online Arabic character's recognition is still low and retain space
for improvement. In this research, an enhanced method of detecting the desired
critical points from vertical and horizontal direction-length of handwriting
stroke features of online Arabic script recognition is proposed. Each extracted
stroke feature divides every isolated character into some meaningful pattern
known as tokens. A minimum feature set is extracted from these tokens for
classification of characters using a multilayer perceptron with a
back-propagation learning algorithm and modified sigmoid function-based
activation function. In this work, two milestones are achieved; firstly, attain
a fixed number of tokens, secondly, minimize the number of the most repetitive
tokens. For experiments, handwritten Arabic characters are selected from the
OHASD benchmark dataset to test and evaluate the proposed method. The proposed
method achieves an average accuracy of 98.6% comparable in state of art
character recognition techniques.Comment: 16 page
Handwritten Bangla Digit Recognition Using Deep Learning
In spite of the advances in pattern recognition technology, Handwritten
Bangla Character Recognition (HBCR) (such as alpha-numeric and special
characters) remains largely unsolved due to the presence of many perplexing
characters and excessive cursive in Bangla handwriting. Even the best existing
recognizers do not lead to satisfactory performance for practical applications.
To improve the performance of Handwritten Bangla Digit Recognition (HBDR), we
herein present a new approach based on deep neural networks which have recently
shown excellent performance in many pattern recognition and machine learning
applications, but has not been throughly attempted for HBDR. We introduce
Bangla digit recognition techniques based on Deep Belief Network (DBN),
Convolutional Neural Networks (CNN), CNN with dropout, CNN with dropout and
Gaussian filters, and CNN with dropout and Gabor filters. These networks have
the advantage of extracting and using feature information, improving the
recognition of two dimensional shapes with a high degree of invariance to
translation, scaling and other pattern distortions. We systematically evaluated
the performance of our method on publicly available Bangla numeral image
database named CMATERdb 3.1.1. From experiments, we achieved 98.78% recognition
rate using the proposed method: CNN with Gabor features and dropout, which
outperforms the state-of-the-art algorithms for HDBR.Comment: 12 pages, 10 figures, 3 table
A Novel Transfer Learning Approach upon Hindi, Arabic, and Bangla Numerals using Convolutional Neural Networks
Increased accuracy in predictive models for handwritten character recognition
will open up new frontiers for optical character recognition. Major drawbacks
of predictive machine learning models are headed by the elongated training time
taken by some models, and the requirement that training and test data be in the
same feature space and consist of the same distribution. In this study, these
obstacles are minimized by presenting a model for transferring knowledge from
one task to another. This model is presented for the recognition of handwritten
numerals in Indic languages. The model utilizes convolutional neural networks
with backpropagation for error reduction and dropout for data overfitting. The
output performance of the proposed neural network is shown to have closely
matched other state-of-the-art methods using only a fraction of time used by
the state-of-the-arts.Comment: 10 pages; 2 figures, 4 tables; conference - International Conference
On Computational Vision and Bio Inspired Computing 2017 (http://iccvbic.com/)
(accepted
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