User Adaptive Handwriting Recognition By Self-Growing Probabilistic Decision-Based Neural Networks  

Oleh:

Pao, H.-T. ; Fu, Hsin-Chia ; Chang, Hung-Yuan ; Yeong, Yuh Xu

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 11 no. 6 (2000), page 1373-1384.
Topik: neural network; adaptive; handwriting; recognition; self - growing; probabilistic decision - based; neural networks

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.4 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

Lihat Detail Induk

Isi artikel

Based on self - growing probabilistic decision - based neural networks (SPDNNs), user adaptation of the parameters of SPDNN is formulated as incremental reinforced and anti - reinforced learning procedures, which are easily integrated into the batched training procedures of the SPDNN. In this study, we developed : 1) an SPDNN based handwriting recognition system ; 2) a two - stage recognition structure ; and 3) a three - phase training methodology for a global coarse classifier (stage 1), a user independent hand written character recognizer (stage 2), and a user adaptation module on a personal computer. With training and testing on a 600 - word commonly used Chinese character set, the recognition results indicate that the user adaptation module significantly improved the recognition accuracy. The average recognition rate increased from 44.2 % to 82.4 % in five adapting cycles, and the performance could finally increase up to 90.2 % in ten adapting cycles.

Opini Anda

Klik untuk menuliskan opini Anda tentang koleksi ini!

Kembali