637 research outputs found
Offline Arabic Handwriting Recognition Using Artificial Neural Network
The ambition of a character recognition system is to transform a text
document typed on paper into a digital format that can be manipulated by word
processor software Unlike other languages, Arabic has unique features, while
other language doesn't have, from this language these are seven or eight
language such as ordo, jewie and Persian writing, Arabic has twenty eight
letters, each of which can be linked in three different ways or separated
depending on the case. The difficulty of the Arabic handwriting recognition is
that, the accuracy of the character recognition which affects on the accuracy
of the word recognition, in additional there is also two or three from for each
character, the suggested solution by using artificial neural network can solve
the problem and overcome the difficulty of Arabic handwriting recognition.Comment: Submitted to Journal of Computer Science and Engineering, see
http://sites.google.com/site/jcseuk/volume-1-issue-1-may-201
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
A Hybrid NN/HMM Modeling Technique for Online Arabic Handwriting Recognition
In this work we propose a hybrid NN/HMM model for online Arabic handwriting
recognition. The proposed system is based on Hidden Markov Models (HMMs) and
Multi Layer Perceptron Neural Networks (MLPNNs). The input signal is segmented
to continuous strokes called segments based on the Beta-Elliptical strategy by
inspecting the extremum points of the curvilinear velocity profile. A neural
network trained with segment level contextual information is used to extract
class character probabilities. The output of this network is decoded by HMMs to
provide character level recognition. In evaluations on the ADAB database, we
achieved 96.4% character recognition accuracy that is statistically
significantly important in comparison with character recognition accuracies
obtained from state-of-the-art online Arabic systems.
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
The State of the Art Recognize in Arabic Script through Combination of Online and Offline
Handwriting recognition refers to the identification of written characters.
Handwriting recognition has become an acute research area in recent years for
the ease of access of computer science. In this paper primarily discussed
On-line and Off-line handwriting recognition methods for Arabic words which are
often used among then across the Middle East and North Africa People. Arabic
word online handwriting recognition is a very challenging task due to its
cursive nature. Because of the characteristic of the whole body of the Arabic
script, namely connectivity between the characters, thereby the segmentation of
An Arabic script is very difficult. In this paper we introduced an Arabic
script multiple classifier system for recognizing notes written on a Starboard.
This Arabic script multiple classifier system combines one off-line and on-line
handwriting recognition systems. The Arabic script recognizers are all based on
Hidden Markov Models but vary in the way of preprocessing and normalization. To
combine the Arabic script output sequences of the recognizers, we incrementally
align the word sequences using a norm string matching algorithm. The Arabic
script combination we could increase the system performance over the excellent
character recognizer by about 3%. The proposed technique is also the necessary
step towards character recognition, person identification, personality
determination where input data is processed from all perspectives.Comment: Pages 7, Figure 6, Table 2. arXiv admin note: text overlap with
arXiv:1110.1488 by other author
Cursive Multilingual Characters Recognition Based on Hard Geometric Features
The cursive nature of multilingual characters segmentation and recognition of
Arabic, Persian, Urdu languages have attracted researchers from academia and
industry. However, despite several decades of research, still multilingual
characters classification accuracy is not up to the mark. This paper presents
an automated approach for multilingual characters segmentation and recognition.
The proposed methodology explores character based on their geometric features.
However, due to uncertainty and without dictionary support few characters are
over-divided. To expand the productivity of the proposed methodology a BPN is
prepared with countless division focuses for cursive multilingual characters.
Prepared BPN separates off base portioned indicates effectively with rapid
upgrade character acknowledgment precision. For reasonable examination, only
benchmark dataset is utilized.Comment: 1
An Extended Beta-Elliptic Model and Fuzzy Elementary Perceptual Codes for Online Multilingual Writer Identification using Deep Neural Network
Actually, the ability to identify the documents authors provides more chances
for using these documents for various purposes. In this paper, we present a new
effective biometric writer identification system from online handwriting. The
system consists of the preprocessing and the segmentation of online handwriting
into a sequence of Beta strokes in a first step. Then, from each stroke, we
extract a set of static and dynamic features from new proposed model that we
called Extended Beta-Elliptic model and from the Fuzzy Elementary Perceptual
Codes. Next, all the segments which are composed of N consecutive strokes are
categorized into groups and subgroups according to their position and their
geometric characteristics. Finally, Deep Neural Network is used as classifier.
Experimental results reveal that the proposed system achieves interesting
results as compared to those of the existing writer identification systems on
Latin and Arabic scripts
Recurrent Neural Network Method in Arabic Words Recognition System
The recognition of unconstrained handwriting continues to be a difficult task
for computers despite active research for several decades. This is because
handwritten text offers great challenges such as character and word
segmentation, character recognition, variation between handwriting styles,
different character size and no font constraints as well as the background
clarity. In this paper primarily discussed Online Handwriting Recognition
methods for Arabic words which being often used among then across the Middle
East and North Africa people. Because of the characteristic of the whole body
of the Arabic words, namely connectivity between the characters, thereby the
segmentation of An Arabic word is very difficult. We introduced a recurrent
neural network to online handwriting Arabic word recognition. The key
innovation is a recently produce recurrent neural networks objective function
known as connectionist temporal classification. The system consists of an
advanced recurrent neural network with an output layer designed for sequence
labeling, partially combined with a probabilistic language model. Experimental
results show that unconstrained Arabic words achieve recognition rates about
79%, which is significantly higher than the about 70% using a previously
developed hidden markov model based recognition system.Comment: 6 Pages, 5 Figures, Vol. 3, Issue 11, pages 43-4
Indic Handwritten Script Identification using Offline-Online Multimodal Deep Network
In this paper, we propose a novel approach of word-level Indic script
identification using only character-level data in training stage. The
advantages of using character level data for training have been outlined in
section I. Our method uses a multimodal deep network which takes both offline
and online modality of the data as input in order to explore the information
from both the modalities jointly for script identification task. We take
handwritten data in either modality as input and the opposite modality is
generated through intermodality conversion. Thereafter, we feed this
offline-online modality pair to our network. Hence, along with the advantage of
utilizing information from both the modalities, it can work as a single
framework for both offline and online script identification simultaneously
which alleviates the need for designing two separate script identification
modules for individual modality. One more major contribution is that we propose
a novel conditional multimodal fusion scheme to combine the information from
offline and online modality which takes into account the real origin of the
data being fed to our network and thus it combines adaptively. An exhaustive
experiment has been done on a data set consisting of English and six Indic
scripts. Our proposed framework clearly outperforms different frameworks based
on traditional classifiers along with handcrafted features and deep learning
based methods with a clear margin. Extensive experiments show that using only
character level training data can achieve state-of-art performance similar to
that obtained with traditional training using word level data in our framework.Comment: Accepted in Information Fusion, Elsevie
Cursive Overlapped Character Segmentation: An Enhanced Approach
Segmentation of highly slanted and horizontally overlapped characters is a
challenging research area that is still fresh. Several techniques are reported
in the state of art, but produce low accuracy for the highly slanted characters
segmentation and cause overall low handwriting recognition precision.
Accordingly, this paper presents a simple yet effective approach for character
segmentation of such difficult slanted cursive words without using any slant
correction technique. Rather a new concept of core-zone is introduced for
segmenting such difficult slanted handwritten words. However, due to the
inherent nature of cursive words, few characters are over-segmented and
therefore, a threshold is selected heuristically to overcome this problem. For
fair comparison, difficult words are extracted from the IAM benchmark database.
Experiments thus performed exhibit promising result and high speed.Comment: 10 Page
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