201,935 research outputs found
Characters as Graphs: Recognizing Online Handwritten Chinese Characters via Spatial Graph Convolutional Network
Chinese is one of the most widely used languages in the world, yet online
handwritten Chinese character recognition (OLHCCR) remains challenging. To
recognize Chinese characters, one popular choice is to adopt the 2D
convolutional neural network (2D-CNN) on the extracted feature images, and
another one is to employ the recurrent neural network (RNN) or 1D-CNN on the
time-series features. Instead of viewing characters as either static images or
temporal trajectories, here we propose to represent characters as geometric
graphs, retaining both spatial structures and temporal orders. Accordingly, we
propose a novel spatial graph convolution network (SGCN) to effectively
classify those character graphs for the first time. Specifically, our SGCN
incorporates the local neighbourhood information via spatial graph convolutions
and further learns the global shape properties with a hierarchical residual
structure. Experiments on IAHCC-UCAS2016, ICDAR-2013, and UNIPEN datasets
demonstrate that the SGCN can achieve comparable recognition performance with
the state-of-the-art methods for character recognition.Comment: 8 pages, 4 figures. A full version of this paper has been submitted
to an international journa
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
Design of a Very Compact CNN Classifier for Online Handwritten Chinese Character Recognition Using DropWeight and Global Pooling
Currently, owing to the ubiquity of mobile devices, online handwritten
Chinese character recognition (HCCR) has become one of the suitable choice for
feeding input to cell phones and tablet devices. Over the past few years,
larger and deeper convolutional neural networks (CNNs) have extensively been
employed for improving character recognition performance. However, its
substantial storage requirement is a significant obstacle in deploying such
networks into portable electronic devices. To circumvent this problem, we
propose a novel technique called DropWeight for pruning redundant connections
in the CNN architecture. It is revealed that the proposed method not only
treats streamlined architectures such as AlexNet and VGGNet well but also
exhibits remarkable performance for deep residual network and inception
network. We also demonstrate that global pooling is a better choice for
building very compact online HCCR systems. Experiments were performed on the
ICDAR-2013 online HCCR competition dataset using our proposed network, and it
is found that the proposed approach requires only 0.57 MB for storage, whereas
state-of-the-art CNN-based methods require up to 135 MB; meanwhile the
performance is decreased only by 0.91%.Comment: 5 pages, 2 figures, 2 table
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
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 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
Building Fast and Compact Convolutional Neural Networks for Offline Handwritten Chinese Character Recognition
Like other problems in computer vision, offline handwritten Chinese character
recognition (HCCR) has achieved impressive results using convolutional neural
network (CNN)-based methods. However, larger and deeper networks are needed to
deliver state-of-the-art results in this domain. Such networks intuitively
appear to incur high computational cost, and require the storage of a large
number of parameters, which renders them unfeasible for deployment in portable
devices. To solve this problem, we propose a Global Supervised Low-rank
Expansion (GSLRE) method and an Adaptive Drop-weight (ADW) technique to solve
the problems of speed and storage capacity. We design a nine-layer CNN for HCCR
consisting of 3,755 classes, and devise an algorithm that can reduce the
networks computational cost by nine times and compress the network to 1/18 of
the original size of the baseline model, with only a 0.21% drop in accuracy. In
tests, the proposed algorithm surpassed the best single-network performance
reported thus far in the literature while requiring only 2.3 MB for storage.
Furthermore, when integrated with our effective forward implementation, the
recognition of an offline character image took only 9.7 ms on a CPU. Compared
with the state-of-the-art CNN model for HCCR, our approach is approximately 30
times faster, yet 10 times more cost efficient.Comment: 15 pages, 7 figures, 5 table
A Review of Research on Devnagari Character Recognition
English Character Recognition (CR) has been extensively studied in the last
half century and progressed to a level, sufficient to produce technology driven
applications. But same is not the case for Indian languages which are
complicated in terms of structure and computations. Rapidly growing
computational power may enable the implementation of Indic CR methodologies.
Digital document processing is gaining popularity for application to office and
library automation, bank and postal services, publishing houses and
communication technology. Devnagari being the national language of India,
spoken by more than 500 million people, should be given special attention so
that document retrieval and analysis of rich ancient and modern Indian
literature can be effectively done. This article is intended to serve as a
guide and update for the readers, working in the Devnagari Optical Character
Recognition (DOCR) area. An overview of DOCR systems is presented and the
available DOCR techniques are reviewed. The current status of DOCR is discussed
and directions for future research are suggested.Comment: 8 pages, 1 Figure, 8 Tables, Journal pape
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
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.
- …