990 research outputs found
Deep Adaptive Learning for Writer Identification based on Single Handwritten Word Images
There are two types of information in each handwritten word image: explicit
information which can be easily read or derived directly, such as lexical
content or word length, and implicit attributes such as the author's identity.
Whether features learned by a neural network for one task can be used for
another task remains an open question. In this paper, we present a deep
adaptive learning method for writer identification based on single-word images
using multi-task learning. An auxiliary task is added to the training process
to enforce the emergence of reusable features. Our proposed method transfers
the benefits of the learned features of a convolutional neural network from an
auxiliary task such as explicit content recognition to the main task of writer
identification in a single procedure. Specifically, we propose a new adaptive
convolutional layer to exploit the learned deep features. A multi-task neural
network with one or several adaptive convolutional layers is trained
end-to-end, to exploit robust generic features for a specific main task, i.e.,
writer identification. Three auxiliary tasks, corresponding to three explicit
attributes of handwritten word images (lexical content, word length and
character attributes), are evaluated. Experimental results on two benchmark
datasets show that the proposed deep adaptive learning method can improve the
performance of writer identification based on single-word images, compared to
non-adaptive and simple linear-adaptive approaches.Comment: Under view of Pattern Recognitio
Offline Handwritten Signature Verification - Literature Review
The area of Handwritten Signature Verification has been broadly researched in
the last decades, but remains an open research problem. The objective of
signature verification systems is to discriminate if a given signature is
genuine (produced by the claimed individual), or a forgery (produced by an
impostor). This has demonstrated to be a challenging task, in particular in the
offline (static) scenario, that uses images of scanned signatures, where the
dynamic information about the signing process is not available. Many
advancements have been proposed in the literature in the last 5-10 years, most
notably the application of Deep Learning methods to learn feature
representations from signature images. In this paper, we present how the
problem has been handled in the past few decades, analyze the recent
advancements in the field, and the potential directions for future research.Comment: Accepted to the International Conference on Image Processing Theory,
Tools and Applications (IPTA 2017
Texture Features from Handwritten Images for Writer Identification
Identification of the writer is having wide scope in emerging technology due to its usage in various types of applications, especially in forensic science and biometric science. Our aim in this project is to identify author or writer from script which is handwritten and obtained as scanned images. Features of textures will be elicitated from wavelet decomposed images based on co-occurrence histograms. These will get (capture) the information about the relations among sub-bands of less frequency and that in sub-bands of higher frequency at the particular level of the transformed image. If the co-relation between the sub-bands has resolution of same then that indicates a stronger relation. Then relationship strength will indicate as information was essential considered to differentiating the textures. The proposed methodology will be executed with English handwritten images by considering 5, 10 penmanship or writers. Ability of features from texture in identifying writers is indicated though the outcome achieved in experimentation
Dissimilarity Gaussian Mixture Models for Efficient Offline Handwritten Text-Independent Identification using SIFT and RootSIFT Descriptors
Handwriting biometrics is the science of identifying the behavioural aspect of an individual’s writing style and exploiting it to develop automated writer identification and verification systems. This paper presents an efficient handwriting identification system which combines Scale Invariant Feature Transform (SIFT) and RootSIFT descriptors in a set of Gaussian mixture models (GMM). In particular, a new concept of similarity and dissimilarity Gaussian mixture models (SGMM and DGMM) is introduced. While a SGMM is constructed for every writer to describe the intra-class similarity that is exhibited between the handwritten texts of the same writer, a DGMM represents the contrast or dissimilarity that exists between the writer’s style on one hand and other different handwriting styles on the other hand. Furthermore, because the handwritten text is described by a number of key point descriptors where each descriptor generates a SGMM/DGMM score, a new weighted histogram method is proposed to derive the intermediate prediction score for each writer’s GMM. The idea of weighted histogram exploits the fact that handwritings from the same writer should exhibit more similar textual patterns than dissimilar ones, hence, by penalizing the bad scores with a cost function, the identification rate can be significantly enhanced. Our proposed system has been extensively assessed using six different public datasets (including three English, two Arabic and one hybrid language) and the results have shown the superiority of the proposed system over state-of-the-art techniques
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
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