3,847 research outputs found
Joint Visual Denoising and Classification using Deep Learning
Visual restoration and recognition are traditionally addressed in pipeline
fashion, i.e. denoising followed by classification. Instead, observing
correlations between the two tasks, for example clearer image will lead to
better categorization and vice visa, we propose a joint framework for visual
restoration and recognition for handwritten images, inspired by advances in
deep autoencoder and multi-modality learning. Our model is a 3-pathway deep
architecture with a hidden-layer representation which is shared by multi-inputs
and outputs, and each branch can be composed of a multi-layer deep model. Thus,
visual restoration and classification can be unified using shared
representation via non-linear mapping, and model parameters can be learnt via
backpropagation. Using MNIST and USPS data corrupted with structured noise, the
proposed framework performs at least 20\% better in classification than
separate pipelines, as well as clearer recovered images. The noise model and
the reproducible source code is available at
{\url{https://github.com/ganggit/jointmodel}}.Comment: 5 pages, 7 figures, ICIP 201
DeepOtsu: Document Enhancement and Binarization using Iterative Deep Learning
This paper presents a novel iterative deep learning framework and apply it
for document enhancement and binarization. Unlike the traditional methods which
predict the binary label of each pixel on the input image, we train the neural
network to learn the degradations in document images and produce the uniform
images of the degraded input images, which allows the network to refine the
output iteratively. Two different iterative methods have been studied in this
paper: recurrent refinement (RR) which uses the same trained neural network in
each iteration for document enhancement and stacked refinement (SR) which uses
a stack of different neural networks for iterative output refinement. Given the
learned uniform and enhanced image, the binarization map can be easy to obtain
by a global or local threshold. The experimental results on several public
benchmark data sets show that our proposed methods provide a new clean version
of the degraded image which is suitable for visualization and promising results
of binarization using the global Otsu's threshold based on the enhanced images
learned iteratively by the neural network.Comment: Accepted by Pattern Recognitio
Learning Human Motion Models for Long-term Predictions
We propose a new architecture for the learning of predictive spatio-temporal
motion models from data alone. Our approach, dubbed the Dropout Autoencoder
LSTM, is capable of synthesizing natural looking motion sequences over long
time horizons without catastrophic drift or motion degradation. The model
consists of two components, a 3-layer recurrent neural network to model
temporal aspects and a novel auto-encoder that is trained to implicitly recover
the spatial structure of the human skeleton via randomly removing information
about joints during training time. This Dropout Autoencoder (D-AE) is then used
to filter each predicted pose of the LSTM, reducing accumulation of error and
hence drift over time. Furthermore, we propose new evaluation protocols to
assess the quality of synthetic motion sequences even for which no ground truth
data exists. The proposed protocols can be used to assess generated sequences
of arbitrary length. Finally, we evaluate our proposed method on two of the
largest motion-capture datasets available to date and show that our model
outperforms the state-of-the-art on a variety of actions, including cyclic and
acyclic motion, and that it can produce natural looking sequences over longer
time horizons than previous methods
CSSL-RHA: Contrastive Self-Supervised Learning for Robust Handwriting Authentication
Handwriting authentication is a valuable tool used in various fields, such as
fraud prevention and cultural heritage protection. However, it remains a
challenging task due to the complex features, severe damage, and lack of
supervision. In this paper, we propose a novel Contrastive Self-Supervised
Learning framework for Robust Handwriting Authentication (CSSL-RHA) to address
these issues. It can dynamically learn complex yet important features and
accurately predict writer identities. Specifically, to remove the negative
effects of imperfections and redundancy, we design an information-theoretic
filter for pre-processing and propose a novel adaptive matching scheme to
represent images as patches of local regions dominated by more important
features. Through online optimization at inference time, the most informative
patch embeddings are identified as the "most important" elements. Furthermore,
we employ contrastive self-supervised training with a momentum-based paradigm
to learn more general statistical structures of handwritten data without
supervision. We conduct extensive experiments on five benchmark datasets and
our manually annotated dataset EN-HA, which demonstrate the superiority of our
CSSL-RHA compared to baselines. Additionally, we show that our proposed model
can still effectively achieve authentication even under abnormal circumstances,
such as data falsification and corruption.Comment: 10 pages, 4 figures, 3 tables, submitted to ACM MM 202
Learning to Read by Spelling: Towards Unsupervised Text Recognition
This work presents a method for visual text recognition without using any
paired supervisory data. We formulate the text recognition task as one of
aligning the conditional distribution of strings predicted from given text
images, with lexically valid strings sampled from target corpora. This enables
fully automated, and unsupervised learning from just line-level text-images,
and unpaired text-string samples, obviating the need for large aligned
datasets. We present detailed analysis for various aspects of the proposed
method, namely - (1) impact of the length of training sequences on convergence,
(2) relation between character frequencies and the order in which they are
learnt, (3) generalisation ability of our recognition network to inputs of
arbitrary lengths, and (4) impact of varying the text corpus on recognition
accuracy. Finally, we demonstrate excellent text recognition accuracy on both
synthetically generated text images, and scanned images of real printed books,
using no labelled training examples
Handwritten Digit Recognition and Classification Using Machine Learning
In this paper, multiple learning techniques based on Optical character recognition (OCR) for the handwritten digit recognition are examined, and a new accuracy level for recognition of the MNIST dataset is reported. The proposed framework involves three primary parts, image pre-processing, feature extraction and classification. This study strives to improve the recognition accuracy by more than 99% in handwritten digit recognition. As will be seen, pre-processing and feature extraction play crucial roles in this experiment to reach the highest accuracy
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