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Collection of on-line handwritten Japanese character pattern databases and their analyses

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TLDR
The design of on-line handwritten Japanese character pattern databases, software tools for pattern collection and verification, and analyses of collected patterns reveal greater variations in stroke count for characters having many strokes, with people generally using fewer strokes.
Abstract
We describe the design of on-line handwritten Japanese character pattern databases, software tools for pattern collection and verification, and analyses of collected patterns. Two databases containing over 3 million patterns were compiled: one with 120 people contributing 12,000 patterns each and another with 163 participants contributing 10,000 patterns each. Patterns were collected mostly in their sentential contexts and verified by machine and human inspection. Their analyses reveal greater variations in stroke count for characters having many strokes, with people generally using fewer strokes; they additionally reveal that stroke order variations are generally caused by common habits and added strokes.

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References
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Journal ArticleDOI

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TL;DR: An image database for handwritten text recognition research is described that contains digital images of approximately 5000 city names, 5000 state names, 10000 ZIP Codes, and 50000 alphanumeric characters to overcome the limitations of earlier databases.
Journal ArticleDOI

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Proceedings ArticleDOI

UNIPEN project of on-line data exchange and recognizer benchmarks

TL;DR: The status of the UNIPEN project of data exchange and recognizer benchmarks started two years ago is reported, to propose and implement solutions to the growing need of handwriting samples for online handwriting recognizers used by pen-based computers.
Proceedings ArticleDOI

The IRESTE On/Off (IRONOFF) dual handwriting database

TL;DR: This work has developed a dual on/off database, named IRONOFF, that contains a large number of isolated characters, digits, and cursive words written by French writers and has been designed so that, given an online point, it can be mapped at the correct location in the corresponding scanned image, and conversely, each offline pixel can be temporally indexed.
Journal ArticleDOI

Handwritten character classification using nearest neighbor in large databases

TL;DR: It is shown that systems built on a simple statistical technique and a large training database can be automatically optimized to produce classification accuracies of 99% in the domain of handwritten digits.
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