256 research outputs found
Leveraging Expert Models for Training Deep Neural Networks in Scarce Data Domains: Application to Offline Handwritten Signature Verification
This paper introduces a novel approach to leverage the knowledge of existing
expert models for training new Convolutional Neural Networks, on domains where
task-specific data are limited or unavailable. The presented scheme is applied
in offline handwritten signature verification (OffSV) which, akin to other
biometric applications, suffers from inherent data limitations due to
regulatory restrictions. The proposed Student-Teacher (S-T) configuration
utilizes feature-based knowledge distillation (FKD), combining graph-based
similarity for local activations with global similarity measures to supervise
student's training, using only handwritten text data. Remarkably, the models
trained using this technique exhibit comparable, if not superior, performance
to the teacher model across three popular signature datasets. More importantly,
these results are attained without employing any signatures during the feature
extraction training process. This study demonstrates the efficacy of leveraging
existing expert models to overcome data scarcity challenges in OffSV and
potentially other related domains
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
Re-ranking for Writer Identification and Writer Retrieval
Automatic writer identification is a common problem in document analysis.
State-of-the-art methods typically focus on the feature extraction step with
traditional or deep-learning-based techniques. In retrieval problems,
re-ranking is a commonly used technique to improve the results. Re-ranking
refines an initial ranking result by using the knowledge contained in the
ranked result, e. g., by exploiting nearest neighbor relations. To the best of
our knowledge, re-ranking has not been used for writer
identification/retrieval. A possible reason might be that publicly available
benchmark datasets contain only few samples per writer which makes a re-ranking
less promising. We show that a re-ranking step based on k-reciprocal nearest
neighbor relationships is advantageous for writer identification, even if only
a few samples per writer are available. We use these reciprocal relationships
in two ways: encode them into new vectors, as originally proposed, or integrate
them in terms of query-expansion. We show that both techniques outperform the
baseline results in terms of mAP on three writer identification datasets
WordStylist: Styled Verbatim Handwritten Text Generation with Latent Diffusion Models
Text-to-Image synthesis is the task of generating an image according to a
specific text description. Generative Adversarial Networks have been considered
the standard method for image synthesis virtually since their introduction;
today, Denoising Diffusion Probabilistic Models are recently setting a new
baseline, with remarkable results in Text-to-Image synthesis, among other
fields. Aside its usefulness per se, it can also be particularly relevant as a
tool for data augmentation to aid training models for other document image
processing tasks. In this work, we present a latent diffusion-based method for
styled text-to-text-content-image generation on word-level. Our proposed method
manages to generate realistic word image samples from different writer styles,
by using class index styles and text content prompts without the need of
adversarial training, writer recognition, or text recognition. We gauge system
performance with Frechet Inception Distance, writer recognition accuracy, and
writer retrieval. We show that the proposed model produces samples that are
aesthetically pleasing, help boosting text recognition performance, and gets
similar writer retrieval score as real data
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