4,160 research outputs found
Toward high-performance online HCCR: a CNN approach with DropDistortion, path signature and spatial stochastic max-pooling
This paper presents an investigation of several techniques that increase the
accuracy of online handwritten Chinese character recognition (HCCR). We propose
a new training strategy named DropDistortion to train a deep convolutional
neural network (DCNN) with distorted samples. DropDistortion gradually lowers
the degree of character distortion during training, which allows the DCNN to
better generalize. Path signature is used to extract effective features for
online characters. Further improvement is achieved by employing spatial
stochastic max-pooling as a method of feature map distortion and model
averaging. Experiments were carried out on three publicly available datasets,
namely CASIA-OLHWDB 1.0, CASIA-OLHWDB 1.1, and the ICDAR2013 online HCCR
competition dataset. The proposed techniques yield state-of-the-art recognition
accuracies of 97.67%, 97.30%, and 97.99%, respectively.Comment: 10 pages, 7 figure
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
MatriVasha: A Multipurpose Comprehensive Database for Bangla Handwritten Compound Characters
At present, recognition of the Bangla handwriting compound character has been
an essential issue for many years. In recent years there have been
application-based researches in machine learning, and deep learning, which is
gained interest, and most notably is handwriting recognition because it has a
tremendous application such as Bangla OCR. MatrriVasha, the project which can
recognize Bangla, handwritten several compound characters. Currently, compound
character recognition is an important topic due to its variant application, and
helps to create old forms, and information digitization with reliability. But
unfortunately, there is a lack of a comprehensive dataset that can categorize
all types of Bangla compound characters. MatrriVasha is an attempt to align
compound character, and it's challenging because each person has a unique style
of writing shapes. After all, MatrriVasha has proposed a dataset that intends
to recognize Bangla 120(one hundred twenty) compound characters that consist of
2552(two thousand five hundred fifty-two) isolated handwritten characters
written unique writers which were collected from within Bangladesh. This
dataset faced problems in terms of the district, age, and gender-based written
related research because the samples were collected that includes a verity of
the district, age group, and the equal number of males, and females. As of now,
our proposed dataset is so far the most extensive dataset for Bangla compound
characters. It is intended to frame the acknowledgment technique for
handwritten Bangla compound character. In the future, this dataset will be made
publicly available to help to widen the research.Comment: 19 fig, 2 tabl
Learning to Write Stylized Chinese Characters by Reading a Handful of Examples
Automatically writing stylized Chinese characters is an attractive yet
challenging task due to its wide applicabilities. In this paper, we propose a
novel framework named Style-Aware Variational Auto-Encoder (SA-VAE) to flexibly
generate Chinese characters. Specifically, we propose to capture the different
characteristics of a Chinese character by disentangling the latent features
into content-related and style-related components. Considering of the complex
shapes and structures, we incorporate the structure information as prior
knowledge into our framework to guide the generation. Our framework shows a
powerful one-shot/low-shot generalization ability by inferring the style
component given a character with unseen style. To the best of our knowledge,
this is the first attempt to learn to write new-style Chinese characters by
observing only one or a few examples. Extensive experiments demonstrate its
effectiveness in generating different stylized Chinese characters by fusing the
feature vectors corresponding to different contents and styles, which is of
significant importance in real-world applications.Comment: Accepted by IJCAI 201
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.
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
Text Line Segmentation of Historical Documents: a Survey
There is a huge amount of historical documents in libraries and in various
National Archives that have not been exploited electronically. Although
automatic reading of complete pages remains, in most cases, a long-term
objective, tasks such as word spotting, text/image alignment, authentication
and extraction of specific fields are in use today. For all these tasks, a
major step is document segmentation into text lines. Because of the low quality
and the complexity of these documents (background noise, artifacts due to
aging, interfering lines),automatic text line segmentation remains an open
research field. The objective of this paper is to present a survey of existing
methods, developed during the last decade, and dedicated to documents of
historical interest.Comment: 25 pages, submitted version, To appear in International Journal on
Document Analysis and Recognition, On line version available at
http://www.springerlink.com/content/k2813176280456k3
Handwritten Isolated Bangla Compound Character Recognition: a new benchmark using a novel deep learning approach
In this work, a novel deep learning technique for the recognition of
handwritten Bangla isolated compound character is presented and a new benchmark
of recognition accuracy on the CMATERdb 3.1.3.3 dataset is reported. Greedy
layer wise training of Deep Neural Network has helped to make significant
strides in various pattern recognition problems. We employ layerwise training
to Deep Convolutional Neural Networks (DCNN) in a supervised fashion and
augment the training process with the RMSProp algorithm to achieve faster
convergence. We compare results with those obtained from standard shallow
learning methods with predefined features, as well as standard DCNNs.
Supervised layerwise trained DCNNs are found to outperform standard shallow
learning models such as Support Vector Machines as well as regular DCNNs of
similar architecture by achieving error rate of 9.67% thereby setting a new
benchmark on the CMATERdb 3.1.3.3 with recognition accuracy of 90.33%,
representing an improvement of nearly 10%
Coverless Information Hiding Based on Generative adversarial networks
Traditional image steganography modifies the content of the image more or
less, it is hard to resist the detection of image steganalysis tools. To
address this problem, a novel method named generative coverless information
hiding method based on generative adversarial networks is proposed in this
paper. The main idea of the method is that the class label of generative
adversarial networks is replaced with the secret information as a driver to
generate hidden image directly, and then extract the secret information from
the hidden image through the discriminator. It's the first time that the
coverless information hiding is achieved by generative adversarial networks.
Compared with the traditional image steganography, this method does not modify
the content of the original image. therefore, this method can resist image
steganalysis tools effectively. In terms of steganographic capacity,
anti-steganalysis, safety and reliability, the experimen shows that this hidden
algorithm performs well.Comment: arXiv admin note: text overlap with arXiv:1703.05502 by other author
A Novel Transfer Learning Approach upon Hindi, Arabic, and Bangla Numerals using Convolutional Neural Networks
Increased accuracy in predictive models for handwritten character recognition
will open up new frontiers for optical character recognition. Major drawbacks
of predictive machine learning models are headed by the elongated training time
taken by some models, and the requirement that training and test data be in the
same feature space and consist of the same distribution. In this study, these
obstacles are minimized by presenting a model for transferring knowledge from
one task to another. This model is presented for the recognition of handwritten
numerals in Indic languages. The model utilizes convolutional neural networks
with backpropagation for error reduction and dropout for data overfitting. The
output performance of the proposed neural network is shown to have closely
matched other state-of-the-art methods using only a fraction of time used by
the state-of-the-arts.Comment: 10 pages; 2 figures, 4 tables; conference - International Conference
On Computational Vision and Bio Inspired Computing 2017 (http://iccvbic.com/)
(accepted
- …