DeepWriteSYN: On-Line Handwriting Synthesis via Deep Short-Term Representations

• URL: http://arxiv.org/abs/2009.06308v2
• Date: Tue, 8 Dec 2020 11:03:11 GMT
• Title: DeepWriteSYN: On-Line Handwriting Synthesis via Deep Short-Term Representations
• Authors: Ruben Tolosana, Paula Delgado-Santos, Andres Perez-Uribe, Ruben Vera-Rodriguez, Julian Fierrez, Aythami Morales
• Abstract summary: DeepWriteSYN is a novel on-line handwriting approach via deep short-term representations. It can generate realistic handwriting variations of a given handwritten structure corresponding to the natural variation within a given population or a given subject.
• Score: 14.498981800711302
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This study proposes DeepWriteSYN, a novel on-line handwriting synthesis approach via deep short-term representations. It comprises two modules: i) an optional and interchangeable temporal segmentation, which divides the handwriting into short-time segments consisting of individual or multiple concatenated strokes; and ii) the on-line synthesis of those short-time handwriting segments, which is based on a sequence-to-sequence Variational Autoencoder (VAE). The main advantages of the proposed approach are that the synthesis is carried out in short-time segments (that can run from a character fraction to full characters) and that the VAE can be trained on a configurable handwriting dataset. These two properties give a lot of flexibility to our synthesiser, e.g., as shown in our experiments, DeepWriteSYN can generate realistic handwriting variations of a given handwritten structure corresponding to the natural variation within a given population or a given subject. These two cases are developed experimentally for individual digits and handwriting signatures, respectively, achieving in both cases remarkable results. Also, we provide experimental results for the task of on-line signature verification showing the high potential of DeepWriteSYN to improve significantly one-shot learning scenarios. To the best of our knowledge, this is the first synthesis approach capable of generating realistic on-line handwriting in the short term (including handwritten signatures) via deep learning. This can be very useful as a module toward long-term realistic handwriting generation either completely synthetic or as natural variation of given handwriting samples.

Related papers

• Scalable Learning of Latent Language Structure With Logical Offline Cycle Consistency [71.42261918225773]
  Conceptually, LOCCO can be viewed as a form of self-learning where the semantic being trained is used to generate annotations for unlabeled text. As an added bonus, the annotations produced by LOCCO can be trivially repurposed to train a neural text generation model.
  arXiv  Detail & Related papers  (2023-05-31T16:47:20Z)
• 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)
• MURMUR: Modular Multi-Step Reasoning for Semi-Structured Data-to-Text Generation [102.20036684996248]
  We propose MURMUR, a neuro-symbolic modular approach to text generation from semi-structured data with multi-step reasoning. We conduct experiments on two data-to-text generation tasks like WebNLG and LogicNLG.
  arXiv  Detail & Related papers  (2022-12-16T17:36:23Z)
• 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)
• Data Incubation -- Synthesizing Missing Data for Handwriting Recognition [16.62493361545184]
  We show how a generative model can be used to build a better recognizer through the control of content and style. We use the framework to optimize data synthesis and demonstrate significant improvement on handwriting recognition over a model trained on real data only.
  arXiv  Detail & Related papers  (2021-10-13T21:28:18Z)
• 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)
• Generating Handwriting via Decoupled Style Descriptors [28.31500214381889]
  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.
  arXiv  Detail & Related papers  (2020-08-26T02:52:48Z)
• Text Recognition in Real Scenarios with a Few Labeled Samples [55.07859517380136]
  Scene text recognition (STR) is still a hot research topic in computer vision field. This paper proposes a few-shot adversarial sequence domain adaptation (FASDA) approach to build sequence adaptation. Our approach can maximize the character-level confusion between the source domain and the target domain.
  arXiv  Detail & Related papers  (2020-06-22T13:03:01Z)
• POINTER: Constrained Progressive Text Generation via Insertion-based Generative Pre-training [93.79766670391618]
  We present POINTER, a novel insertion-based approach for hard-constrained text generation. The proposed method operates by progressively inserting new tokens between existing tokens in a parallel manner. The resulting coarse-to-fine hierarchy makes the generation process intuitive and interpretable.
  arXiv  Detail & Related papers  (2020-05-01T18:11:54Z)
• Spatio-Temporal Handwriting Imitation [11.54523121769666]
  Subdividing the process into smaller subtasks makes it possible to imitate someone's handwriting with a high chance to be visually indistinguishable for humans. We show that also a typical writer identification system can partially be fooled by the created fake handwritings.
  arXiv  Detail & Related papers  (2020-03-24T00:46:40Z)

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.