Neural Network Applications in Character Recognition and Document Analysis

Jackel, L. D.; Battista, Marco; Ben, Jan; Bromley, Jane; Burges, Christopher J. C.; Baird, Henry S.; Cosatto, Eric; Denker, John S.; Graf, Hans Peter; Katseff, Howard P.; LeCun, Yann; Nohl, C.R.; Sackinger, E.; Shamilian, J. H.; Shoemaker, T.; Stenard, C. E.; Strom, B. I.; Rui, Ting; Wood, Thomas H.; Zuraw, C. R.

doi:10.1007/978-1-4615-2734-3_14

Cited by 4 publications

(1 citation statement)

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“…'Proving grounds ', 1992-1996 I turn now to the consolidation of reductions in error rates and an acceleration in industrial deployments of the group's systems by interrogating Holmdel's distinction between 'proving grounds' and 'successful applications'. 52 In doing so, I demonstrate that internal deployments at AT&T and in partnership with companies owned by the telecommunications giant were used to prove the value of character recognition artificial neural networks throughout this period. The section concludes by illustrating that commercial applications continued to rely on the creation of new roles that corrected system errors before proposing that industrial deployments collided with increases in speed and performance to energize imaginaries of mind and machine in the middle of the decade.…”

Section: From Specialization To Generalization 1990-1992mentioning

confidence: 93%

Bell Labs and the ‘neural’ network, 1986–1996

Law

2023

BJHS Themes

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Between 1986 and 1996 researchers at the AT&T Bell Laboratories Adaptive Systems Research Department curated thousands of images of handwritten digits assembled by the United States Postal Service to train and evaluate artificial neural networks. In academic papers and conference literature, and in conversations with the press, Bell Labs researchers, executives and company spokespeople deployed the language of neurophysiology to position the systems as capable of codifying and reproducing feats of perception. Interpretations such as these were pivotal to the formation of brain–computer imaginaries that surrounded the development of the systems, which obscured the institutional infrastructures, clerical and cognitive labour, and the manipulation and maintenance of data on which feats of ‘recognition’ depended. Central to building the group's networks was the development of data sets constructed in consort with the US Postal Service, which arbitrated between the practicality of conducting research and the representation of an extensive catalogue of possible forms and permutations of handwritten digits. These imaginaries, which stressed a likeness with the human brain, were compounded by the promotion of ‘successful applications’ that took place under the AT&T corporate umbrella and with the essential support of US Postal Service workers to correct system errors.

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Section: From Specialization To Generalization 1990-1992mentioning

confidence: 93%

Bell Labs and the ‘neural’ network, 1986–1996

Law

2023

BJHS Themes

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Feedforward Neural Nets

Fine

1999

Wiley Encyclopedia of Electrical and Electronics Engineering

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The sections in this article are Neural Network Elements and Notation The Literature Roles for Artificial Neural Networks Mathematical Setup for Feedforward Neural Networks Basic Properties of the Representation by Neural Networks The Representational Power of a Single‐Hidden‐Layer Network Training a Neural Network: Background and Error Surface Training: Backpropagation Descent Algorithms Trends and Open Problems

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Sensor-assisted multiple access protocols for wireless networks

Fine

Wicker²,

Berger³

ICUPC '98. IEEE 1998 International Conference on Universal Personal Communications. Conference Proceedings (Cat. No.98TH8384)

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Neural Network Applications in Character Recognition and Document Analysis

Cited by 4 publications

References 4 publications

Bell Labs and the ‘neural’ network, 1986–1996

Bell Labs and the ‘neural’ network, 1986–1996

Feedforward Neural Nets

Sensor-assisted multiple access protocols for wireless networks

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