9,631 research outputs found
Trajectory-based Radical Analysis Network for Online Handwritten Chinese Character Recognition
Recently, great progress has been made for online handwritten Chinese
character recognition due to the emergence of deep learning techniques.
However, previous research mostly treated each Chinese character as one class
without explicitly considering its inherent structure, namely the radical
components with complicated geometry. In this study, we propose a novel
trajectory-based radical analysis network (TRAN) to firstly identify radicals
and analyze two-dimensional structures among radicals simultaneously, then
recognize Chinese characters by generating captions of them based on the
analysis of their internal radicals. The proposed TRAN employs recurrent neural
networks (RNNs) as both an encoder and a decoder. The RNN encoder makes full
use of online information by directly transforming handwriting trajectory into
high-level features. The RNN decoder aims at generating the caption by
detecting radicals and spatial structures through an attention model. The
manner of treating a Chinese character as a two-dimensional composition of
radicals can reduce the size of vocabulary and enable TRAN to possess the
capability of recognizing unseen Chinese character classes, only if the
corresponding radicals have been seen. Evaluated on CASIA-OLHWDB database, the
proposed approach significantly outperforms the state-of-the-art
whole-character modeling approach with a relative character error rate (CER)
reduction of 10%. Meanwhile, for the case of recognition of 500 unseen Chinese
characters, TRAN can achieve a character accuracy of about 60% while the
traditional whole-character method has no capability to handle them
Handwritten Bangla Character Recognition Using The State-of-Art Deep Convolutional Neural Networks
In spite of advances in object recognition technology, Handwritten Bangla
Character Recognition (HBCR) remains largely unsolved due to the presence of
many ambiguous handwritten characters and excessively cursive Bangla
handwritings. Even the best existing recognizers do not lead to satisfactory
performance for practical applications related to Bangla character recognition
and have much lower performance than those developed for English alpha-numeric
characters. To improve the performance of HBCR, we herein present the
application of the state-of-the-art Deep Convolutional Neural Networks (DCNN)
including VGG Network, All Convolution Network (All-Conv Net), Network in
Network (NiN), Residual Network, FractalNet, and DenseNet for HBCR. The deep
learning approaches have the advantage of extracting and using feature
information, improving the recognition of 2D shapes with a high degree of
invariance to translation, scaling and other distortions. We systematically
evaluated the performance of DCNN models on publicly available Bangla
handwritten character dataset called CMATERdb and achieved the superior
recognition accuracy when using DCNN models. This improvement would help in
building an automatic HBCR system for practical applications.Comment: 12 pages,22 figures, 5 tables. arXiv admin note: text overlap with
arXiv:1705.0268
DenseRAN for Offline Handwritten Chinese Character Recognition
Recently, great success has been achieved in offline handwritten Chinese
character recognition by using deep learning methods. Chinese characters are
mainly logographic and consist of basic radicals, however, previous research
mostly treated each Chinese character as a whole without explicitly considering
its internal two-dimensional structure and radicals. In this study, we propose
a novel radical analysis network with densely connected architecture (DenseRAN)
to analyze Chinese character radicals and its two-dimensional structures
simultaneously. DenseRAN first encodes input image to high-level visual
features by employing DenseNet as an encoder. Then a decoder based on recurrent
neural networks is employed, aiming at generating captions of Chinese
characters by detecting radicals and two-dimensional structures through
attention mechanism. The manner of treating a Chinese character as a
composition of two-dimensional structures and radicals can reduce the size of
vocabulary and enable DenseRAN to possess the capability of recognizing unseen
Chinese character classes, only if the corresponding radicals have been seen in
training set. Evaluated on ICDAR-2013 competition database, the proposed
approach significantly outperforms whole-character modeling approach with a
relative character error rate (CER) reduction of 18.54%. Meanwhile, for the
case of recognizing 3277 unseen Chinese characters in CASIA-HWDB1.2 database,
DenseRAN can achieve a character accuracy of about 41% while the traditional
whole-character method has no capability to handle them.Comment: Accepted by ICFHR201
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
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
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
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.
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
Word Searching in Scene Image and Video Frame in Multi-Script Scenario using Dynamic Shape Coding
Retrieval of text information from natural scene images and video frames is a
challenging task due to its inherent problems like complex character shapes,
low resolution, background noise, etc. Available OCR systems often fail to
retrieve such information in scene/video frames. Keyword spotting, an
alternative way to retrieve information, performs efficient text searching in
such scenarios. However, current word spotting techniques in scene/video images
are script-specific and they are mainly developed for Latin script. This paper
presents a novel word spotting framework using dynamic shape coding for text
retrieval in natural scene image and video frames. The framework is designed to
search query keyword from multiple scripts with the help of on-the-fly
script-wise keyword generation for the corresponding script. We have used a
two-stage word spotting approach using Hidden Markov Model (HMM) to detect the
translated keyword in a given text line by identifying the script of the line.
A novel unsupervised dynamic shape coding based scheme has been used to group
similar shape characters to avoid confusion and to improve text alignment.
Next, the hypotheses locations are verified to improve retrieval performance.
To evaluate the proposed system for searching keyword from natural scene image
and video frames, we have considered two popular Indic scripts such as Bangla
(Bengali) and Devanagari along with English. Inspired by the zone-wise
recognition approach in Indic scripts[1], zone-wise text information has been
used to improve the traditional word spotting performance in Indic scripts. For
our experiment, a dataset consisting of images of different scenes and video
frames of English, Bangla and Devanagari scripts were considered. The results
obtained showed the effectiveness of our proposed word spotting approach.Comment: Multimedia Tools and Applications, Springe
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
- …