A Radical-Partitioned Coded Block Adaptive Neural Network Structure for Large-Volume Chinese Characters Recognition
نویسنده
چکیده
This paper presents a coded block adaptive neural network system using a radical-partitioned structure for large-volume Chinese characters recognition VLSI. Using the coded block adaptive neural network system with a radical-partitioned structure, 1000 frequently-used Chinese characters have been successfully trained in 139.2 hours using an 18 MIPS computer. According to the simulation results, the coded block system with a radical-partitioned structure provides an acceptable learning time, a good recognition rate and an excellent expansion capability for large-volume Chinese characters recognition using VLSI.
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