Recovering Dynamic Stroke Information of Multi-stroke Handwritten Characters with Complex Patterns

Nagoya, Takayuki; Fujioka, Hikaru

doi:10.1109/icfhr.2012.258

Cited by 4 publications

(4 citation statements)

References 4 publications

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“…Several approaches have been proposed for recovering the dynamics from static handwritten traces, some exploiting global or local characteristics of the ink shape, (Boccignone et al (1993); Doermann and Rosenfeld (1995); Plamondon and Privitera (1999)), others mapping somehow the ink to a graph and reformulate the trajectory recovery as a graph traversal one (Bunke et al (1997); Dinh et al (2016); Jager (1996); Kato and Yasuhara (2000); Nagoya and Fujioka (2012); Phan et al (2015); Qiao et al (2006); Diaz et al (2021)). Hidden Markov Model (Viard-Gaudin et al (2005)), genetic algorithms (Elbaati et al (2009)) and convolutional neural networks (Zhao et al (2018); Elbaati et al (2019); Kumarbhunia et al (2018); Nguyen et al (2020); Rabhi et al (2021); Sumi et al (2019); Zhang et al (2019)) have also been proposed.…”

Section: Related Workmentioning

confidence: 99%

“…Other proposed techniques have been evaluated only on single-stroke traces, i.e. traces performed without lifting the pen from the paper (Kato and Yasuhara (2000); Nagoya and Fujioka (2011); Qiao et al (2006)), or on single characters (Jager (1996) (Boccignone et al (1993); Kato and Yasuhara (1999); Nagoya and Fujioka (2012); Yu Qiao and Yasuhara (2006)).…”

Section: Related Workmentioning

confidence: 99%

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A biologically inspired approach for recovering the trajectory of offline handwriting

Senatore

Santoro²,

Parziale

et al. 2023

Memetic Comp.

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Background: Reconstructing the trajectory from the static image of handwritten ink traces is useful in many practical applications envisaging handwriting analysis and recognition from off-line data, as it allows to use methods, algorithms and tools that deal with on-line data, achieving better results than those achieved on off-line data. Methods: In this work we addressed the trajectory recovery by proposing an approach inspired by the processes involved in human learning of motor skills to perform voluntary and complex movements. As humans learn motor skills by a trial-and-error process driven by the performance and the consumption of metabolic energy, our approach generates a trajectory and estimates the consumption of metabolic energy needed to execute it, and in case it is deemed too energy demanding, a new one is generated and its energy consumption is evaluated. Eventually the one corresponding to the minimum energy consumption among the extant ones is selected as the actual one. A biologically inspired approach for recovering the trajectory of off-line handwritingResults and Conclusions: The effectiveness of the proposed approach has been quantitatively and extensively evaluated on a large and publicly available dataset, containing multi-stroke words. The experimental results show that our approach outperforms the existing ones in terms of Root Mean Square Error and Dynamic Time Warping distance between the recovered trajectories and the actual ones. Furthermore, an on-line recognition system provided with the trajectory recovered from off-line samples showed an overall reduction of about 1% with respect to the recognition rate achieved by the system when provided with on-line data.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A biologically inspired approach for recovering the trajectory of offline handwriting

Senatore

Santoro²,

Parziale

et al. 2023

Memetic Comp.

View full text Add to dashboard Cite

show abstract

Section: Related Workmentioning

confidence: 99%

A biologically inspired approach for recovering the trajectory of off-line handwriting

Senatore

Santoro²,

Parziale

et al. 2022

Preprint

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Background: Reconstructing the trajectory from the static image of handwritten ink traces is useful in many practical applications envisaging handwriting analysis and recognition from off-line data, as it allows to use methods, algorithms and tools that deal with on-line data, achieving better results than those achieved on off-line data.Methods: In this work we addressed the trajectory recovery by proposing an approach inspired by the processes involved in human learning of motor skills to perform voluntary and complex movements. As humans learn motor skills by a trial-and-error process driven by the performance and the consumption of metabolic energy, our approach generates a trajectory and estimates the consumption of metabolic energy needed to execute it, and in case it is deemed too energy demanding, a new one is generated and its energy consumption is evaluated. Eventually the one corresponding to the minimum energy consumption among the extant ones is selected as the actual one. Results and Conclusions: The effectiveness of the proposed approach has been quantitatively and extensively evaluated on a large and publicly available dataset, containing multi-stroke words. The experimental results show that our approach outperforms the existing ones in terms of Root Mean Square Error and Dynamic Time Warping distance between the recovered trajectories and the actual ones. Furthermore, an on-line recognition system provided with the trajectory recovered from off-line samples showed an overall reduction of about 1% with respect to the recognition rate achieved by the system when provided with on-line data.

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“…In order to prevent explosion of combinations, Kato et al [22] restricted themselves to single-stroke script subjecting to certain assumptions on junctions, where strokes can be extracted by traversal of graph. Nagoya et al [23] extended the technique to multi-stroke script under assumptions on how strokes are intersected.…”

Section: Stroke Extractionmentioning

confidence: 99%

Stroke Extraction for Offline Handwritten Mathematical Expression Recognition

Chan

2020

IEEE Access

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Offline handwritten mathematical expression recognition is often considered much harder than its online counterpart due to the absence of temporal information and the presence of background noise. In order to take advantage of the more developed techniques on online recognition and save resources, an oversegmentation approach is proposed to recover strokes from a textual bitmap image automatically. The proposed algorithm first break down the skeleton of a binarized image into junctions and segments, then segments are merged to form strokes, finally the ordering is determined by recursive projection and topological sort.Given a state-of-art online handwritten mathematical expression recognition system, the proposed procedure correctly recognized 58.22%, 65.65% and 65.05% of the offline formulas rendered from CROHME 2014, 2016 and 2019 respectively. Therefore, the effectiveness of stroke extraction to offline recognition is justified.

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Recovering Dynamic Stroke Information of Multi-stroke Handwritten Characters with Complex Patterns

Cited by 4 publications

References 4 publications

A biologically inspired approach for recovering the trajectory of offline handwriting

A biologically inspired approach for recovering the trajectory of offline handwriting

A biologically inspired approach for recovering the trajectory of off-line handwriting

Stroke Extraction for Offline Handwritten Mathematical Expression Recognition

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