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Proceedings ArticleDOI

Stroke Level User-Adaptation for Stroke Order Free Online Handwriting Recognition

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TLDR
A novel lightweight user-adaptive online handwriting recognition scheme based on prior identification of the set of various strokes of different shapes used in writing the characters of the underlying alphabet utilizing a representative sample database is presented.
Abstract
In this article, a novel lightweight user-adaptive online handwriting recognition scheme has been presented. The present recognition approach is stroke order free. It is based on prior identification of the set of various strokes of different shapes used in writing the characters of the underlying alphabet utilizing a representative sample database. In this approach a very small number of prototypes of each stroke shape is used along with certain weighted DTW distance based nearest neighbour classifier to recognize the strokes in the input character. Individual characters are identified using a look-up table (LUT) each row of which corresponds to one character composed of a distinct set of stroke shapes. This LUT is formed using the representative sample set of the underlying character set. If a stroke does not find a close match in the training set or if the set of strokes for an input character does not find a corresponding entry in the LUT, user adaptation takes place using a modified Learning Vector Quantization (LVQ) method. The proposed scheme has been implemented in an Android-based Bangla handwriting recognizer [1] for handheld devices and its recognition performance is encouraging.

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Citations
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Journal ArticleDOI

A survey of mono- and multi-lingual character recognition using deep and shallow architectures: indic and non-indic scripts

TL;DR: A detailed review and analysis of the work done in multilingual online as well as offline CR for Indic and non-Indic scripts is presented and the major deficiencies in monolingual and multilingual CR for printed and handwritten text are identified.
Proceedings Article

Character Recognition using Approaches of Artificial Neural Network: A Review

TL;DR: A survey on ANNs applied for recognizing characters and the features applied as inputs is presented in this article , where the principal objective is to help researchers attempt to apply ANN for character recognition and analysis of the networks and features extracted to improve classification accuracy.
Journal Article

On-line Hindi Handwritten Character Recognition for Mobile Devices

TL;DR: In this article, the authors presented an online handwritten isolated character recognition system for an Indian language, Hindi, for mobile devices, which is implemented on mobile device using two different approaches namely Principal Component Analysis (PCA) and Dynamic Time Wrapping (DTW).
References
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Journal ArticleDOI

Online and off-line handwriting recognition: a comprehensive survey

TL;DR: The nature of handwritten language, how it is transduced into electronic data, and the basic concepts behind written language recognition algorithms are described.
Journal ArticleDOI

Weighted dynamic time warping for time series classification

TL;DR: A novel distance measure, called a weighted DTW (WDTW), which is a penalty-based DTW that penalizes points with higher phase difference between a reference point and a testing point in order to prevent minimum distance distortion caused by outliers is proposed.
Proceedings ArticleDOI

SHARK2: a large vocabulary shorthand writing system for pen-based computers

TL;DR: The architecture, algorithms and interfaces of a high-capacity multi-channel pen-gesture recognition system that supports a gradual and seamless transition from visually guided tracing to recall-based gesturing are designed and implemented.
Journal ArticleDOI

Self-Organizing Maps and Learning Vector Quantization forFeature Sequences

TL;DR: The Self-Organizing Map (SOM) and Learning Vector Quantization (LVQ) algorithms are constructed in this work for variable-length and warped feature sequences and good results have been obtained in speaker-independent speech recognition.
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