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A Bayesian computational model for online character recognition and disability assessment during cursive eye writing.

Diard J, Rynik V, Lorenceau J - Front Psychol (2013)

Bottom Line: Preliminary experimental results are presented, which illustrate the method, showing the feasibility of character recognition in the context of eye writing.We then show experimentally how a model of the unknown character can be used to detect trajectories that are likely to be new symbols, and how disability assessment can be performed by opportunistically observing characteristics of fine motor control, as letter are being traced.Experimental analyses also help identify specificities of eye writing, as compared to handwriting, and the resulting technical challenges.

View Article: PubMed Central - PubMed

Affiliation: Laboratoire de Psychologie et NeuroCognition, Université Grenoble Alpes-CNRS Grenoble, France.

ABSTRACT
This research involves a novel apparatus, in which the user is presented with an illusion inducing visual stimulus. The user perceives illusory movement that can be followed by the eye, so that smooth pursuit eye movements can be sustained in arbitrary directions. Thus, free-flow trajectories of any shape can be traced. In other words, coupled with an eye-tracking device, this apparatus enables "eye writing," which appears to be an original object of study. We adapt a previous model of reading and writing to this context. We describe a probabilistic model called the Bayesian Action-Perception for Eye On-Line model (BAP-EOL). It encodes probabilistic knowledge about isolated letter trajectories, their size, high-frequency components of the produced trajectory, and pupil diameter. We show how Bayesian inference, in this single model, can be used to solve several tasks, like letter recognition and novelty detection (i.e., recognizing when a presented character is not part of the learned database). We are interested in the potential use of the eye writing apparatus by motor impaired patients: the final task we solve by Bayesian inference is disability assessment (i.e., measuring and tracking the evolution of motor characteristics of produced trajectories). Preliminary experimental results are presented, which illustrate the method, showing the feasibility of character recognition in the context of eye writing. We then show experimentally how a model of the unknown character can be used to detect trajectories that are likely to be new symbols, and how disability assessment can be performed by opportunistically observing characteristics of fine motor control, as letter are being traced. Experimental analyses also help identify specificities of eye writing, as compared to handwriting, and the resulting technical challenges.

No MeSH data available.


Related in: MedlinePlus

Example of character recognition. Right: the trajectory presented as input to the system, and the via-points detected along this trajectory. Left: probability distributions over letters as via-points are detected (the darker the color, the higher the probability): the first column is the probability distribution P(L /C1Δx, C1Δy, C1ẋ, C1ẏ), the second column is P(L /C1:2Δx, C1:2Δy, C1:2ẋ, C1:2ẏ), etc., and the final column is P(L /C1:15Δx, C1:15Δy, C1:15ẋ, C1:15ẏ, Sx, Sy, A).
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Figure 7: Example of character recognition. Right: the trajectory presented as input to the system, and the via-points detected along this trajectory. Left: probability distributions over letters as via-points are detected (the darker the color, the higher the probability): the first column is the probability distribution P(L /C1Δx, C1Δy, C1ẋ, C1ẏ), the second column is P(L /C1:2Δx, C1:2Δy, C1:2ẋ, C1:2ẏ), etc., and the final column is P(L /C1:15Δx, C1:15Δy, C1:15ẋ, C1:15ẏ, Sx, Sy, A).

Mentions: Example of a trajectory for the letter “h” and the corresponding via-points positions. Via-points, where x and y velocities are zeroed, perceptually corresponds to extremes portions of the trajectories (i.e., bottom, top, left, and right borders of the local curvature). Notice that, after the first point of the trajectory, a via-point appears to be missing: it was filtered out by a geometric constraint that ensures that via-points are not too close to each other. Notice also that via-points sometimes appear out of the trajectory (and also in Figures 6, 7, 10, 14): this is because we visualize here via-point positions in the low precision, discrete domains of the corresponding probabilistic variables (see the experimental Results section).


A Bayesian computational model for online character recognition and disability assessment during cursive eye writing.

Diard J, Rynik V, Lorenceau J - Front Psychol (2013)

Example of character recognition. Right: the trajectory presented as input to the system, and the via-points detected along this trajectory. Left: probability distributions over letters as via-points are detected (the darker the color, the higher the probability): the first column is the probability distribution P(L /C1Δx, C1Δy, C1ẋ, C1ẏ), the second column is P(L /C1:2Δx, C1:2Δy, C1:2ẋ, C1:2ẏ), etc., and the final column is P(L /C1:15Δx, C1:15Δy, C1:15ẋ, C1:15ẏ, Sx, Sy, A).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3822325&req=5

Figure 7: Example of character recognition. Right: the trajectory presented as input to the system, and the via-points detected along this trajectory. Left: probability distributions over letters as via-points are detected (the darker the color, the higher the probability): the first column is the probability distribution P(L /C1Δx, C1Δy, C1ẋ, C1ẏ), the second column is P(L /C1:2Δx, C1:2Δy, C1:2ẋ, C1:2ẏ), etc., and the final column is P(L /C1:15Δx, C1:15Δy, C1:15ẋ, C1:15ẏ, Sx, Sy, A).
Mentions: Example of a trajectory for the letter “h” and the corresponding via-points positions. Via-points, where x and y velocities are zeroed, perceptually corresponds to extremes portions of the trajectories (i.e., bottom, top, left, and right borders of the local curvature). Notice that, after the first point of the trajectory, a via-point appears to be missing: it was filtered out by a geometric constraint that ensures that via-points are not too close to each other. Notice also that via-points sometimes appear out of the trajectory (and also in Figures 6, 7, 10, 14): this is because we visualize here via-point positions in the low precision, discrete domains of the corresponding probabilistic variables (see the experimental Results section).

Bottom Line: Preliminary experimental results are presented, which illustrate the method, showing the feasibility of character recognition in the context of eye writing.We then show experimentally how a model of the unknown character can be used to detect trajectories that are likely to be new symbols, and how disability assessment can be performed by opportunistically observing characteristics of fine motor control, as letter are being traced.Experimental analyses also help identify specificities of eye writing, as compared to handwriting, and the resulting technical challenges.

View Article: PubMed Central - PubMed

Affiliation: Laboratoire de Psychologie et NeuroCognition, Université Grenoble Alpes-CNRS Grenoble, France.

ABSTRACT
This research involves a novel apparatus, in which the user is presented with an illusion inducing visual stimulus. The user perceives illusory movement that can be followed by the eye, so that smooth pursuit eye movements can be sustained in arbitrary directions. Thus, free-flow trajectories of any shape can be traced. In other words, coupled with an eye-tracking device, this apparatus enables "eye writing," which appears to be an original object of study. We adapt a previous model of reading and writing to this context. We describe a probabilistic model called the Bayesian Action-Perception for Eye On-Line model (BAP-EOL). It encodes probabilistic knowledge about isolated letter trajectories, their size, high-frequency components of the produced trajectory, and pupil diameter. We show how Bayesian inference, in this single model, can be used to solve several tasks, like letter recognition and novelty detection (i.e., recognizing when a presented character is not part of the learned database). We are interested in the potential use of the eye writing apparatus by motor impaired patients: the final task we solve by Bayesian inference is disability assessment (i.e., measuring and tracking the evolution of motor characteristics of produced trajectories). Preliminary experimental results are presented, which illustrate the method, showing the feasibility of character recognition in the context of eye writing. We then show experimentally how a model of the unknown character can be used to detect trajectories that are likely to be new symbols, and how disability assessment can be performed by opportunistically observing characteristics of fine motor control, as letter are being traced. Experimental analyses also help identify specificities of eye writing, as compared to handwriting, and the resulting technical challenges.

No MeSH data available.


Related in: MedlinePlus