A Framework for Adaptation of the Active-DTW Classifier for Online Handwritten Character Recognition

Roy, Vandana; Madhvanath, Sriganesh; Anand, Sujatha Jamuna; Sharma, Rajiv Kumar

doi:10.1109/icdar.2009.195

Cited by 4 publications

(2 citation statements)

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“…This attribute makes Hopfield network a feedback network in that the output of every neuron can feed to other neurons in the same level. The evolution of Hopfield network is a nonlinear dynamic process, which can be described by nonlinear difference equations (discrete network) or nonlinear differential equations (continuous network) [3,4].…”

Section: Hopfield Neural Networkmentioning

confidence: 99%

On-Line Handwriting Recognition Based on Hopfield Neural Network

Zhang

Li²,

Ren

et al. 2014

AMM

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In this paper, Discrete Hopfield Neural Network (DHNN) is adopted to realize handwritten characters recognition. First, learning samples are preprocessed including binarization, normalization and interpolation. Then pixel features are extracted and used to establish DHNN. The handwritten test samples and noise corrupted samples are finally inputted into the network to verify its recognition performance. Simulation results reveal that DHNN has good fault tolerance and disturbance rejection performance. In addition, the recognition system is realized with MATLAB neural network toolbox and GUI, which verifies the feasibility of the algorithm.

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Section: Hopfield Neural Networkmentioning

confidence: 99%

On-Line Handwriting Recognition Based on Hopfield Neural Network

Zhang

Li²,

Ren

et al. 2014

AMM

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“…However, when a sample is not recognized with high confidence and the user provides feedback about the intended class using the context menu, the sample is added to the prototype set only if maximum prototype count for that class has not been reached. Otherwise it is used to modify its nearest sample among the existing prototypes [16]. In addition, successfully recognized samples are also used to modify the existing prototypes.…”

Section: Adaptationmentioning

confidence: 99%

GeCCo: Finger gesture-based command and control for touch interfaces

Madhvanath

Mandalapu

Madan

et al. 2012

2012 4th International Conference on Intelligent Human Computer Interaction (IHCI)

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gesture recognition; touch gestures; mode-switching; adaptation With touch-based interfaces becoming commonplace on personal computing devices ranging from phones and slates to notebook and desktop PCs, a number of common tasks that were once performed using mouse or keyboard input now need to be performed using fingers on the touch surface. Finger-drawn gestures offer a viable alternative to desktop and keyboard shortcuts as shortcuts for common tasks such as launching of applications and navigation of large media collections. In order to be truly effective, the interface for definition, management and invocation of gestures should be highly intuitive, and optimized for the device. In particular, the process of invoking gestures should be seamless and natural. Further, the recognition of gestures needs to be robust for the specific user. In this paper, we describe GeCCo (Gesture Command and Control), a system for personalized finger gesture shortcuts for touch-enabled desktops and trackpad-enabled notebook PCs. One of the key issues addressed in the design of GeCCo is that of mode switching in the context of notebook PCs. We describe a user study to decide between different interactions for mode switching. The interactions are designed such that mode switch and gesture can be simultaneously indicated. Since new gestures may be defined by the user at any time, statistical pattern classification techniques which require large numbers of training samples for each gesture are not useful. Instead we use nearest-neighbor classification with Dynamic Time Warping (DTW) distance, and a writer adaptation scheme for improving accuracy to desired levels. We conclude the paper with experimental results and some thoughts on next steps. Abstract-With touch-based interfaces becoming mainstream on personal computing devices ranging from phones and slates to even notebook and desktop PCs, a number of common tasks that were once performed using mouse or keyboard input now need to be performed using fingers on the touch surface. In this paper, we focus on the use of finger gestures as shortcuts for tasks such as command and control of applications, navigation of large collections. A keyboard shortcut or desktop icon for an application may be substituted with a finger gesture e.g. tracing a gesture 'O' launches MS Outlook. Such gestures may be used even on a conventional notebook PC using the trackpad. However in order to be truly effective, the interface for definition, management and invocation of gestures should be highly intuitive, and customized to the available device hardware. In particular, it should be possible for the user to define and modify gestures as and when required, and the process of invoking gestures should be seamless and natural. Further, the recognition of gestures needs to be robust for the specific user. We describe GeCCo (Gesture Command and Control), an application prototype we have created for touch-enabled devices that allows the user to define his or her own gesture shortcuts for applications. One of t...

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