The State of the Art in Online Handwriting Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Precise Candidate Selection for Large Character Set Recognition by Confidence Evaluation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Structure Extraction from Decorated Characters Using Multiscale Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Off-Line Handwritten Chinese Character Recognition with Nonlinear Pre-classification
ICMI '00 Proceedings of the Third International Conference on Advances in Multimodal Interfaces
Analysis and Recognition of Asian Scripts - the State of the Art
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Offline handwritten Chinese character recognition by radical decomposition
ACM Transactions on Asian Language Information Processing (TALIP)
Online Chinese Character Recognition System with Handwritten Pinyin Input
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
A fuzzy and rough sets approach for recognition of handwritten Thai characters
ISPRA'06 Proceedings of the 5th WSEAS International Conference on Signal Processing, Robotics and Automation
SACH'06 Proceedings of the 2006 conference on Arabic and Chinese handwriting recognition
SACH'06 Proceedings of the 2006 conference on Arabic and Chinese handwriting recognition
Proceedings of the 2011 Joint Workshop on Multilingual OCR and Analytics for Noisy Unstructured Text Data
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One of the most challenging topics is the recognition of Chinese handwriting, especially offline recognition. In this paper, an offline recognition system based on multifeature and multilevel classification is presented for handwritten Chinese characters. Ten classes of multifeatures, such as peripheral shape features, stroke density features, and stroke direction features, are used in this system. The multilevel classification scheme consists of a group classifier and a five-level character classifier, where two new technologies, overlap clustering and Gaussian distribution selector, are developed. Experiments have been conducted to recognize 5,401 daily-used Chinese characters. The recognition rate is about 90 percent for a unique candidate, and 98 percent for multichoice with 10 candidates.