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CURSIVE WORD RECOGNITION BASED ON INTERACTIVE ACTIVATION AND EARLY VISUAL PROCESSING MODELS
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DOI：10.1142/S0129065708001683.png)
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Abstract
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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.
Keywords:
Handwriting recognition
interactive activation model
feedforward model
convolutional neural networks
reading models
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