Character Recognition Digit Recognition Pattern Recognition Spatiotemporal Neural Networks Modular Networks Segmentation Problem
作者:
Lokendra Shastri,
Thomas Fontaine,
期刊:
Connection Science
(Taylor Available online 1995)
卷期:
Volume 7,
issue 3-4
页码: 211-246
ISSN:0954-0091
年代: 1995
DOI:10.1080/09540099509696192
出版商: Taylor & Francis Group
关键词: Character Recognition Digit Recognition Pattern Recognition Spatiotemporal Neural Networks Modular Networks Segmentation Problem
数据来源: Taylor
摘要:
We describe an alternate approach to visual recognition of handwritten words, wherein an image is converted into a spatio-temporal signal by scanning it in one or more directions, and processed by a suitable connectionist network. The scheme offers several attractive features including shift-invariance, explication of local spatial geometry along the scan direction, a significant reduction in the number of free parameters, the ability to process arbitrarily long images along the scan direction, and a natural framework for dealing with the segmentation/recognition dilemma. Other salient features of the work include the use of a modular and structured approach for network construction and the integration of connectionist components with a procedural component to exploit the complementary strengths of both techniques. The system consists of two connectionist components and a procedural controller. One network concurrently makes recognition and segmentation hypotheses, and another performs refined recognition of segmented characters. The interaction between the networks is governed by the procedural controller. The system is tested on three tasks: isolated digit recognition, recognition of overlapping pairs of digits and recognition of ZIP codes.
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