2,148 research outputs found
Recurrent neural networks based Indic word-wise script identification using character-wise training
This paper presents a novel methodology of Indic handwritten script
recognition using Recurrent Neural Networks and addresses the problem of script
recognition in poor data scenarios, such as when only character level online
data is available. It is based on the hypothesis that curves of online
character data comprise sufficient information for prediction at the word
level. Online character data is used to train RNNs using BLSTM architecture
which are then used to make predictions of online word level data. These
prediction results on the test set are at par with prediction results of models
trained with online word data, while the training of the character level model
is much less data intensive and takes much less time. Performance for
binary-script models and then 5 Indic script models are reported, along with
comparison with HMM models.The system is extended for offline data prediction.
Raw offline data lacks the temporal information available in online data and
required for prediction using models trained with online data. To overcome
this, stroke recovery is implemented and the strokes are utilized for
predicting using the online character level models. The performance on
character and word level offline data is reported.Comment: Version accepted at ICPRS 201
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
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.
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
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
A Hough Transform based Technique for Text Segmentation
Text segmentation is an inherent part of an OCR system irrespective of the
domain of application of it. The OCR system contains a segmentation module
where the text lines, words and ultimately the characters must be segmented
properly for its successful recognition. The present work implements a Hough
transform based technique for line and word segmentation from digitized images.
The proposed technique is applied not only on the document image dataset but
also on dataset for business card reader system and license plate recognition
system. For standardization of the performance of the system the technique is
also applied on public domain dataset published in the website by CMATER,
Jadavpur University. The document images consist of multi-script printed and
hand written text lines with variety in script and line spacing in single
document image. The technique performs quite satisfactorily when applied on
mobile camera captured business card images with low resolution. The usefulness
of the technique is verified by applying it in a commercial project for
localization of license plate of vehicles from surveillance camera images by
the process of segmentation itself. The accuracy of the technique for word
segmentation, as verified experimentally, is 85.7% for document images, 94.6%
for business card images and 88% for surveillance camera images
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
A Complete Workflow for Development of Bangla OCR
Developing a Bangla OCR requires bunch of algorithm and methods. There were
many effort went on for developing a Bangla OCR. But all of them failed to
provide an error free Bangla OCR. Each of them has some lacking. We discussed
about the problem scope of currently existing Bangla OCR's. In this paper, we
present the basic steps required for developing a Bangla OCR and a complete
workflow for development of a Bangla OCR with mentioning all the possible
algorithms required
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 digit Recognition using Support Vector Machine
Handwritten Numeral recognition plays a vital role in postal automation
services especially in countries like India where multiple languages and
scripts are used Discrete Hidden Markov Model (HMM) and hybrid of Neural
Network (NN) and HMM are popular methods in handwritten word recognition
system. The hybrid system gives better recognition result due to better
discrimination capability of the NN. A major problem in handwriting recognition
is the huge variability and distortions of patterns. Elastic models based on
local observations and dynamic programming such HMM are not efficient to absorb
this variability. But their vision is local. But they cannot face to length
variability and they are very sensitive to distortions. Then the SVM is used to
estimate global correlations and classify the pattern. Support Vector Machine
(SVM) is an alternative to NN. In Handwritten recognition, SVM gives a better
recognition result. The aim of this paper is to develop an approach which
improve the efficiency of handwritten recognition using artificial neural
networkComment: 7 pag
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