TY  - JOUR
AU  - Tanaka, Deepak
AU  - Gupta, Farhana
TI  - Handwritten Character Recognition using K-Means Clustering
JO  - Open Access Journal of Engineering Applications
PY  - 2007
VL  - 31
IS  - 2
SP  - 213
EP  - 232
AB  - Both academic and industrial interest in handwritten character recognition have grown alongside the modernisation of urban traffic networks. The formulation relies on k-means clustering, which is well suited to the nonlinear nature of the problem. The proposed method reduces the error by about 59% compared with the conventional approach. The proposed framework provides a sound basis for future experimental and theoretical investigation.
KW  - handwritten character recognition
KW  - k-means clustering
KW  - power quality
KW  - voltage stability
KW  - wireless sensor networks
SN  - 3105-7004
DO  - 10.55555/oajea.2007.3102.012
UR  - https://journal.bsaps.com/article/8429
ER  -