Generating Handwriting via Decoupled Style Descriptors
- URL: http://arxiv.org/abs/2008.11354v2
- Date: Mon, 14 Sep 2020 22:50:41 GMT
- Title: Generating Handwriting via Decoupled Style Descriptors
- Authors: Atsunobu Kotani, Stefanie Tellex, James Tompkin
- Abstract summary: We introduce the Decoupled Style Descriptor model for handwriting.
It factors both character- and writer-level styles and allows our model to represent an overall greater space of styles.
In experiments, our generated results were preferred over a state of the art baseline method 88% of the time.
- Score: 28.31500214381889
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Representing a space of handwriting stroke styles includes the challenge of
representing both the style of each character and the overall style of the
human writer. Existing VRNN approaches to representing handwriting often do not
distinguish between these different style components, which can reduce model
capability. Instead, we introduce the Decoupled Style Descriptor (DSD) model
for handwriting, which factors both character- and writer-level styles and
allows our model to represent an overall greater space of styles. This approach
also increases flexibility: given a few examples, we can generate handwriting
in new writer styles, and also now generate handwriting of new characters
across writer styles. In experiments, our generated results were preferred over
a state of the art baseline method 88% of the time, and in a writer
identification task on 20 held-out writers, our DSDs achieved 89.38% accuracy
from a single sample word. Overall, DSDs allows us to improve both the quality
and flexibility over existing handwriting stroke generation approaches.
Related papers
- StyleDistance: Stronger Content-Independent Style Embeddings with Synthetic Parallel Examples [48.44036251656947]
Style representations aim to embed texts with similar writing styles closely and texts with different styles far apart, regardless of content.
We introduce StyleDistance, a novel approach to training stronger content-independent style embeddings.
arXiv Detail & Related papers (2024-10-16T17:25:25Z) - DiffusionPen: Towards Controlling the Style of Handwritten Text Generation [7.398476020996681]
DiffusionPen (DiffPen) is a 5-shot style handwritten text generation approach based on Latent Diffusion Models.
Our approach captures both textual and stylistic characteristics of seen and unseen words and styles, generating realistic handwritten samples.
Our method outperforms existing methods qualitatively and quantitatively, and its additional generated data can improve the performance of Handwriting Text Recognition (HTR) systems.
arXiv Detail & Related papers (2024-09-09T20:58:25Z) - StylusAI: Stylistic Adaptation for Robust German Handwritten Text Generation [4.891597567642704]
StylusAI is designed to adapt and integrate the stylistic nuances of one language's handwriting into another.
To support the development and evaluation of StylusAI, we present the lqDeutscher Handschriften-Datensatzrq(DHSD) dataset.
arXiv Detail & Related papers (2024-07-22T13:08:30Z) - Disentangling Writer and Character Styles for Handwriting Generation [8.33116145030684]
We present the style-disentangled Transformer (SDT), which employs two complementary contrastive objectives to extract the style commonalities of reference samples.
Our empirical findings reveal that the two learned style representations provide information at different frequency magnitudes.
arXiv Detail & Related papers (2023-03-26T14:32:02Z) - Towards Writing Style Adaptation in Handwriting Recognition [0.0]
We explore models with writer-dependent parameters which take the writer's identity as an additional input.
We propose a Writer Style Block (WSB), an adaptive instance normalization layer conditioned on learned embeddings of the partitions.
We show that our approach outperforms a baseline with no WSB in a writer-dependent scenario and that it is possible to estimate embeddings for new writers.
arXiv Detail & Related papers (2023-02-13T12:36:17Z) - Few-shot Font Generation by Learning Style Difference and Similarity [84.76381937516356]
We propose a novel font generation approach by learning the Difference between different styles and the Similarity of the same style (DS-Font)
Specifically, we propose a multi-layer style projector for style encoding and realize a distinctive style representation via our proposed Cluster-level Contrastive Style (CCS) loss.
arXiv Detail & Related papers (2023-01-24T13:57:25Z) - PART: Pre-trained Authorship Representation Transformer [64.78260098263489]
Authors writing documents imprint identifying information within their texts: vocabulary, registry, punctuation, misspellings, or even emoji usage.
Previous works use hand-crafted features or classification tasks to train their authorship models, leading to poor performance on out-of-domain authors.
We propose a contrastively trained model fit to learn textbfauthorship embeddings instead of semantics.
arXiv Detail & Related papers (2022-09-30T11:08:39Z) - SLOGAN: Handwriting Style Synthesis for Arbitrary-Length and
Out-of-Vocabulary Text [35.83345711291558]
We propose a novel method that can synthesize parameterized and controllable handwriting Styles for arbitrary-Length and Out-of-vocabulary text.
We embed the text content by providing an easily obtainable printed style image, so that the diversity of the content can be flexibly achieved.
Our method can synthesize words that are not included in the training vocabulary and with various new styles.
arXiv Detail & Related papers (2022-02-23T12:13:27Z) - Letter-level Online Writer Identification [86.13203975836556]
We focus on a novel problem, letter-level online writer-id, which requires only a few trajectories of written letters as identification cues.
A main challenge is that a person often writes a letter in different styles from time to time.
We refer to this problem as the variance of online writing styles (Var-O-Styles)
arXiv Detail & Related papers (2021-12-06T07:21:53Z) - Scalable Font Reconstruction with Dual Latent Manifolds [55.29525824849242]
We propose a deep generative model that performs typography analysis and font reconstruction.
Our approach enables us to massively scale up the number of character types we can effectively model.
We evaluate on the task of font reconstruction over various datasets representing character types of many languages.
arXiv Detail & Related papers (2021-09-10T20:37:43Z) - Handwriting Transformers [98.3964093654716]
We propose a transformer-based styled handwritten text image generation approach, HWT, that strives to learn both style-content entanglement and global and local writing style patterns.
The proposed HWT captures the long and short range relationships within the style examples through a self-attention mechanism.
Our proposed HWT generates realistic styled handwritten text images and significantly outperforms the state-of-the-art demonstrated.
arXiv Detail & Related papers (2021-04-08T17:59:43Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.