Handwriting synthesis: classifications and techniques

Elarian, Yousef; Abdel-Aal, R.E.; Ahmad, Irfan; Parvez, Mohammad Tanvir; Zidouri, Abdelmalek

doi:10.1007/s10032-014-0231-x

Cited by 27 publications

(11 citation statements)

References 51 publications

(162 reference statements)

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“…Its relationship with texture synthesis is discussed first, followed by handwriting research. Recently, Elarian et al [2014] provided a wide ranging review of handwriting synthesis methods. See Figure 2 for an overview of the key handwriting terms used in this article.…”

Section: Related Workmentioning

confidence: 99%

My Text in Your Handwriting

2016

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There are many scenarios where we wish to imitate a specific author's penon-paper handwriting style. Rendering new text in someone's handwriting is difficult because natural handwriting is highly variable, yet follows both intentional and involuntary structure that makes a person's style selfconsistent. The variability means that naive example-based texture synthesis can be conspicuously repetitive.We propose an algorithm that renders a desired input string in an author's handwriting. An annotated sample of the author's handwriting is required; the system is flexible enough that historical documents can usually be used with only a little extra effort. Experiments show that our glyphcentric approach, with learned parameters for spacing, line thickness, and pressure, produces novel images of handwriting that look hand-made to casual observers, even when printed on paper.

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Section: Related Workmentioning

confidence: 99%

My Text in Your Handwriting

2016

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“…Our work touches upon various subjects including HCI (e.g., [22,39,44,56]), handwriting analysis [43] and machine learning (e.g., [15,18,28,42]).…”

Section: Related Workmentioning

confidence: 99%

DeepWriting

Aksan

Pece

Hilliges

2018

Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems

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Figure 1: We propose a novel generative neural network architecture that is capable of disentangling style from content and thus makes digital ink editable. Our model can synthesize handwriting from typed text while giving users control over the visual appearance (A), transfer style across handwriting samples (B, solid line box synthesized stroke, dotted line box reference style), and even edit handwritten samples at the word level (C). ABSTRACTDigital ink promises to combine the flexibility and aesthetics of handwriting and the ability to process, search and edit digital text. Character recognition converts handwritten text into a digital representation, albeit at the cost of losing personalized appearance due to the technical difficulties of separating the interwoven components of content and style. In this paper, we propose a novel generative neural network architecture that is capable of disentangling style from content and thus making digital ink editable. Our model can synthesize arbitrary text, while giving users control over the visual appearance (style). For example, allowing for style transfer without changing the content, editing of digital ink at the word level and other application scenarios such as spell-checking and correction of handwritten text. We furthermore contribute a new dataset of handwritten text with fine-grained annotations at the character level and report results from an initial user evaluation.

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“…There are several applications of text synthesis, such as word spotting, CAPTCHAs, character recognition improvement, calligraphy, and others [ 2 ]. Accordingly there are different approaches of synthesis.…”

Section: Related Workmentioning

confidence: 99%

“…For real handwriting databases this has to be done in a time consuming manual or semimanual way. This is one of the reasons, why data synthesis recently gained more and more interest [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

ASM Based Synthesis of Handwritten Arabic Text Pages

Dinges

Al-Hamadi

Elzobi

et al. 2015

The Scientific World Journal

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Document analysis tasks, as text recognition, word spotting, or segmentation, are highly dependent on comprehensive and suitable databases for training and validation. However their generation is expensive in sense of labor and time. As a matter of fact, there is a lack of such databases, which complicates research and development. This is especially true for the case of Arabic handwriting recognition, that involves different preprocessing, segmentation, and recognition methods, which have individual demands on samples and ground truth. To bypass this problem, we present an efficient system that automatically turns Arabic Unicode text into synthetic images of handwritten documents and detailed ground truth. Active Shape Models (ASMs) based on 28046 online samples were used for character synthesis and statistical properties were extracted from the IESK-arDB database to simulate baselines and word slant or skew. In the synthesis step ASM based representations are composed to words and text pages, smoothed by B-Spline interpolation and rendered considering writing speed and pen characteristics. Finally, we use the synthetic data to validate a segmentation method. An experimental comparison with the IESK-arDB database encourages to train and test document analysis related methods on synthetic samples, whenever no sufficient natural ground truthed data is available.

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Handwriting synthesis: classifications and techniques

Cited by 27 publications

References 51 publications

My Text in Your Handwriting

My Text in Your Handwriting

DeepWriting

ASM Based Synthesis of Handwritten Arabic Text Pages

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