7,370 research outputs found
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
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
Drawing and Recognizing Chinese Characters with Recurrent Neural Network
Recent deep learning based approaches have achieved great success on
handwriting recognition. Chinese characters are among the most widely adopted
writing systems in the world. Previous research has mainly focused on
recognizing handwritten Chinese characters. However, recognition is only one
aspect for understanding a language, another challenging and interesting task
is to teach a machine to automatically write (pictographic) Chinese characters.
In this paper, we propose a framework by using the recurrent neural network
(RNN) as both a discriminative model for recognizing Chinese characters and a
generative model for drawing (generating) Chinese characters. To recognize
Chinese characters, previous methods usually adopt the convolutional neural
network (CNN) models which require transforming the online handwriting
trajectory into image-like representations. Instead, our RNN based approach is
an end-to-end system which directly deals with the sequential structure and
does not require any domain-specific knowledge. With the RNN system (combining
an LSTM and GRU), state-of-the-art performance can be achieved on the
ICDAR-2013 competition database. Furthermore, under the RNN framework, a
conditional generative model with character embedding is proposed for
automatically drawing recognizable Chinese characters. The generated characters
(in vector format) are human-readable and also can be recognized by the
discriminative RNN model with high accuracy. Experimental results verify the
effectiveness of using RNNs as both generative and discriminative models for
the tasks of drawing and recognizing Chinese characters
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
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
Large Vocabulary Arabic Online Handwriting Recognition System
Arabic handwriting is a consonantal and cursive writing. The analysis of
Arabic script is further complicated due to obligatory dots/strokes that are
placed above or below most letters and usually written delayed in order. Due to
ambiguities and diversities of writing styles, recognition systems are
generally based on a set of possible words called lexicon. When the lexicon is
small, recognition accuracy is more important as the recognition time is
minimal. On the other hand, recognition speed as well as the accuracy are both
critical when handling large lexicons. Arabic is rich in morphology and syntax
which makes its lexicon large. Therefore, a practical online handwriting
recognition system should be able to handle a large lexicon with reasonable
performance in terms of both accuracy and time. In this paper, we introduce a
fully-fledged Hidden Markov Model (HMM) based system for Arabic online
handwriting recognition that provides solutions for most of the difficulties
inherent in recognizing the Arabic script. A new preprocessing technique for
handling the delayed strokes is introduced. We use advanced modeling techniques
for building our recognition system from the training data to provide more
detailed representation for the differences between the writing units, minimize
the variances between writers in the training data and have a better
representation for the features space. System results are enhanced using an
additional post-processing step with a higher order language model and
cross-word HMM models. The system performance is evaluated using two different
databases covering small and large lexicons. Our system outperforms the
state-of-art systems for the small lexicon database. Furthermore, it shows
promising results (accuracy and time) when supporting large lexicon with the
possibility for adapting the models for specific writers to get even better
results.Comment: Preprint submitted to Pattern Analysis and Applications Journa
A Study of Sindhi Related and Arabic Script Adapted languages Recognition
A large number of publications are available for the Optical Character
Recognition (OCR). Significant researches, as well as articles are present for
the Latin, Chinese and Japanese scripts. Arabic script is also one of mature
script from OCR perspective. The adaptive languages which share Arabic script
or its extended characters; still lacking the OCRs for their language. In this
paper we present the efforts of researchers on Arabic and its related and
adapted languages. This survey is organized in different sections, in which
introduction is followed by properties of Sindhi Language. OCR process
techniques and methods used by various researchers are presented. The last
section is dedicated for future work and conclusion is also discussed.Comment: 11 pages, 8 Figures, Sindh Univ. Res. Jour. (Sci. Ser.
DeepWriter: A Multi-Stream Deep CNN for Text-independent Writer Identification
Text-independent writer identification is challenging due to the huge
variation of written contents and the ambiguous written styles of different
writers. This paper proposes DeepWriter, a deep multi-stream CNN to learn deep
powerful representation for recognizing writers. DeepWriter takes local
handwritten patches as input and is trained with softmax classification loss.
The main contributions are: 1) we design and optimize multi-stream structure
for writer identification task; 2) we introduce data augmentation learning to
enhance the performance of DeepWriter; 3) we introduce a patch scanning
strategy to handle text image with different lengths. In addition, we find that
different languages such as English and Chinese may share common features for
writer identification, and joint training can yield better performance.
Experimental results on IAM and HWDB datasets show that our models achieve high
identification accuracy: 99.01% on 301 writers and 97.03% on 657 writers with
one English sentence input, 93.85% on 300 writers with one Chinese character
input, which outperform previous methods with a large margin. Moreover, our
models obtain accuracy of 98.01% on 301 writers with only 4 English alphabets
as input.Comment: This article will be presented at ICFHR 201
A Two Stage Classification Approach for Handwritten Devanagari Characters
The paper presents a two stage classification approach for handwritten
devanagari characters The first stage is using structural properties like
shirorekha, spine in character and second stage exploits some intersection
features of characters which are fed to a feedforward neural network. Simple
histogram based method does not work for finding shirorekha, vertical bar
(Spine) in handwritten devnagari characters. So we designed a differential
distance based technique to find a near straight line for shirorekha and spine.
This approach has been tested for 50000 samples and we got 89.12% succes
Multistage Hybrid Arabic/Indian Numeral OCR System
The use of OCR in postal services is not yet universal and there are still
many countries that process mail sorting manually. Automated Arabic/Indian
numeral Optical Character Recognition (OCR) systems for Postal services are
being used in some countries, but still there are errors during the mail
sorting process, thus causing a reduction in efficiency. The need to
investigate fast and efficient recognition algorithms/systems is important so
as to correctly read the postal codes from mail addresses and to eliminate any
errors during the mail sorting stage. The objective of this study is to
recognize printed numerical postal codes from mail addresses. The proposed
system is a multistage hybrid system which consists of three different feature
extraction methods, i.e., binary, zoning, and fuzzy features, and three
different classifiers, i.e., Hamming Nets, Euclidean Distance, and Fuzzy Neural
Network Classifiers. The proposed system, systematically compares the
performance of each of these methods, and ensures that the numerals are
recognized correctly. Comprehensive results provide a very high recognition
rate, outperforming the other known developed methods in literature.Comment: IEEE Publication format, International Journal of Computer Science
and Information Security, IJCSIS, Vol. 8 No. 1, April 2010, USA. ISSN 1947
5500, http://sites.google.com/site/ijcsis
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