34,620 research outputs found
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 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 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
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
Improving patch-based scene text script identification with ensembles of conjoined networks
This paper focuses on the problem of script identification in scene text
images. Facing this problem with state of the art CNN classifiers is not
straightforward, as they fail to address a key characteristic of scene text
instances: their extremely variable aspect ratio. Instead of resizing input
images to a fixed aspect ratio as in the typical use of holistic CNN
classifiers, we propose here a patch-based classification framework in order to
preserve discriminative parts of the image that are characteristic of its
class. We describe a novel method based on the use of ensembles of conjoined
networks to jointly learn discriminative stroke-parts representations and their
relative importance in a patch-based classification scheme. Our experiments
with this learning procedure demonstrate state-of-the-art results in two public
script identification datasets. In addition, we propose a new public benchmark
dataset for the evaluation of multi-lingual scene text end-to-end reading
systems. Experiments done in this dataset demonstrate the key role of script
identification in a complete end-to-end system that combines our script
identification method with a previously published text detector and an
off-the-shelf OCR engine
A New COLD Feature based Handwriting Analysis for Ethnicity/Nationality Identification
Identifying crime for forensic investigating teams when crimes involve people
of different nationals is challenging. This paper proposes a new method for
ethnicity (nationality) identification based on Cloud of Line Distribution
(COLD) features of handwriting components. The proposed method, at first,
explores tangent angle for the contour pixels in each row and the mean of
intensity values of each row in an image for segmenting text lines. For
segmented text lines, we use tangent angle and direction of base lines to
remove rule lines in the image. We use polygonal approximation for finding
dominant points for contours of edge components. Then the proposed method
connects the nearest dominant points of every dominant point, which results in
line segments of dominant point pairs. For each line segment, the proposed
method estimates angle and length, which gives a point in polar domain. For all
the line segments, the proposed method generates dense points in polar domain,
which results in COLD distribution. As character component shapes change,
according to nationals, the shape of the distribution changes. This observation
is extracted based on distance from pixels of distribution to Principal Axis of
the distribution. Then the features are subjected to an SVM classifier for
identifying nationals. Experiments are conducted on a complex dataset, which
show the proposed method is effective and outperforms the existing methodComment: Accepted in ICFHR1
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
Lipi Gnani - A Versatile OCR for Documents in any Language Printed in Kannada Script
A Kannada OCR, named Lipi Gnani, has been designed and developed from
scratch, with the motivation of it being able to convert printed text or poetry
in Kannada script, without any restriction on vocabulary. The training and test
sets have been collected from over 35 books published between the period 1970
to 2002, and this includes books written in Halegannada and pages containing
Sanskrit slokas written in Kannada script. The coverage of the OCR is nearly
complete in the sense that it recognizes all the punctuation marks, special
symbols, Indo-Arabic and Kannada numerals and also the interspersed English
words. Several minor and major original contributions have been done in
developing this OCR at the different processing stages such as binarization,
line and character segmentation, recognition and Unicode mapping. This has
created a Kannada OCR that performs as good as, and in some cases, better than
the Google's Tesseract OCR, as shown by the results. To the knowledge of the
authors, this is the maiden report of a complete Kannada OCR, handling all the
issues involved. Currently, there is no dictionary based postprocessing, and
the obtained results are due solely to the recognition process. Four benchmark
test databases containing scanned pages from books in Kannada, Sanskrit,
Konkani and Tulu languages, but all of them printed in Kannada script, have
been created. The word level recognition accuracy of Lipi Gnani is 4% higher on
the Kannada dataset than that of Google's Tesseract OCR, 8% higher on the
datasets of Tulu and Sanskrit, and 25% higher on the Konkani dataset.Comment: 21 pages, 16 figures, 12 tables, submitted to ACM Transactions on
Asian and Low-Resource Language Information Processin
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.
Bangla Text Recognition from Video Sequence: A New Focus
Extraction and recognition of Bangla text from video frame images is
challenging due to complex color background, low-resolution etc. In this paper,
we propose an algorithm for extraction and recognition of Bangla text form such
video frames with complex background. Here, a two-step approach has been
proposed. First, the text line is segmented into words using information based
on line contours. First order gradient value of the text blocks are used to
find the word gap. Next, a local binarization technique is applied on each word
and text line is reconstructed using those words. Secondly, this binarized text
block is sent to OCR for recognition purpose
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