Title :
Recognition of unconstrained handwritten numeral strings using decision value generator
Author :
Kye Kyung Kim ; Chung, Un Koo ; Jin Ho Kim ; Suen, Ching Y.
Author_Institution :
Electron. & Telecommun. Res. Inst., Taejon, South Korea
fDate :
6/23/1905 12:00:00 AM
Abstract :
This paper presents recognition of unconstrained handwritten numeral strings using a decision value generator. The numeral string recognition system is composed of three modules: pre-segmentation, segmentation and recognition. The pre-segmentation module classifies a numeral string into sub-images, such as isolated digits, touching digits or broken digits, based on the confidence value of decision value generator. The segmentation module splits the touching digits using the reliability value of decision value generator. Both segmentation-based and segmentation free methods are used in classification and segmentation. To evaluate the proposed method, experiments were conducted using the handwritten numeral strings of NIST SD19 and a higher recognition performance than previous works was obtained
Keywords :
decision theory; handwritten character recognition; learning (artificial intelligence); probability; reliability; NIST SDl9; confidence value; decision value generator; handwritten numeral string recognition; image segmentation; learning; pattern classification; reliability; Databases; Handwriting recognition; Merging; NIST; Pattern recognition; Postal services;
Conference_Titel :
Document Analysis and Recognition, 2001. Proceedings. Sixth International Conference on
Conference_Location :
Seattle, WA
Print_ISBN :
0-7695-1263-1
DOI :
10.1109/ICDAR.2001.953746