Cursive word recognition based on interactive activation and early visual processing models.
We present an off-line cursive word recognition system based completely on neural networks: reading models and models of early visual processing. The first stage (normalization) preprocesses the input image in order to reduce letter position uncertainty; the second stage (feature extraction) is based on the feedforward model of orientation selectivity; the third stage (letter pre-recognition) is based on a convolutional neural network, and the last stage (word recognition) is based on the interactive activation model.
Ruiz-Pinales
Jose
J
Communications and Electronics, Universidad de Guanajuato, Salamanca, Gto., Mexico. pinales@salamanca.ugto.mx
Jaime-Rivas
Rene
R
Lecolinet
Eric
E
Castro-Bleda
Maria Jose
MJ
D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D006236:Handwriting; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D009415:Nerve Net / Q000502:physiology; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000592:standards*; D010364:Pattern Recognition, Visual / Q000502:physiology; D011932:Reading; D013997:Time Factors; D014793:Visual Cortex / Q000502:physiology; D014795:Visual Pathways / Q000502:physiology