On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Continuous HMM applied to quantization of on-line Korean character spaces
Pattern Recognition Letters
Writer Adaptation for Online Handwriting Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
On-Line Adaptation in Recognition of Handwritten Alphanumeric Characters
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
Substroke Approach to HMM-Based On-line Kanji Handwriting Recognition
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
Recognition of Cursive Roman Handwriting - Past, Present and Future
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Online Recognition of Chinese Characters: The State-of-the-Art
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comparison of Elastic Matching Algorithms for Online Tamil Handwritten Character Recognition
IWFHR '04 Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition
Machine Recognition of Online Handwritten Devanagari Characters
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
On-line Arabic handwriting recognition with templates
Pattern Recognition
Improving a DTW-Based Recognition Engine for On-line Handwritten Characters by Using MLPs
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
On-line Handwriting Recognition of Indian Scripts - The First Benchmark
ICFHR '10 Proceedings of the 2010 12th International Conference on Frontiers in Handwriting Recognition
An overview of character recognition focused on off-line handwriting
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
HMM Based Online Handwritten Bangla Character Recognition Using Dirichlet Distributions
ICFHR '12 Proceedings of the 2012 International Conference on Frontiers in Handwriting Recognition
Building a Personal Handwriting Recognizer on an Android Device
ICFHR '12 Proceedings of the 2012 International Conference on Frontiers in Handwriting Recognition
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Here, we present our recent attempt to develop a lightweight handwriting recognizer suitable for resource constrained handheld devices. Such an application requires real-time recognition of handwritten characters produced on their touchscreens. The proposed approach is well suited for minimal user-lag on devices having only limited computing power in sharp contrast to standard laptops or desktop computers. Moreover, the approach is user-adaptive in the sense that it can adapt through user corrections to wrong predictions. With an increasing number of interactive corrections by the user, the recognition accuracy improves significantly. An input stroke is first re-sampled generating a fixed small number of sample points such that at most two critical points (points corresponding to high curvature) are preserved. We use their x- and y-coordinates as the feature vector and do not compute any other high-level feature vector. The squared Mahalanobis distance is used to identify each stroke of the input sample as one of several stroke categories pre-determined based on a large pool of training samples. The inverted covariance matrix and mean vector for a stroke class that are required for computing the Mahalanobis distance are pre-calculated and stored as Serialized Objects on the SD card of the device. A Look-Up Table (LUT) of stroke combinations as keys and corresponding character class as values is used for the final Unicode character output. In case of an incorrect character output, user corrections are used to automatically update the LUT adapting to the user's particular handwriting style.