title: Cursive word recognition based on interactive activation and early visual processing models.
abstract: 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.
authors.affiliation: Communications and Electronics, Universidad de Guanajuato, Salamanca, Gto., Mexico. pinales@salamanca.ugto.mx
authors.forename: Jose
authors.initials: J
authors.lastname: Ruiz-Pinales
authors.forename: Rene
authors.initials: R
authors.lastname: Jaime-Rivas
authors.forename: Eric
authors.initials: E
authors.lastname: Lecolinet
authors.forename: Maria Jose
authors.initials: MJ
authors.lastname: Castro-Bleda
mesh_terms: 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