Historical milestone · 1998
Gradient-based document recognition / LeNet-5
Reviewed through September 18, 2026
1998 · Historical milestone
Gradient-based document recognition / LeNet-5
- Era
- 1990s
- Theme
- Machine-learning foundations
- Evidence form
- Trained convolutional networks end to end on handwritten digit recognition and integrated them into document-processing systems
- School / paradigm
- Convolutional neural networks
- Institution / context
- Bell Labs
- Researchers
- Yann LeCun; Léon Bottou; Yoshua Bengio; Patrick Haffner
Researcher index
Yoshua Bengio
Neural representation and language learning · Université de Montréal
Neural probabilistic language model; deep learning
Why it still matters. Connected distributed word vectors with probabilistic next-word prediction.
Representative source for this researcher — not necessarily the source of this milestone: https://www.jmlr.org/papers/v3/bengio03a.html (opens in a new tab)
School of thought
Connectionism
Matched on representative researcher.
Cognition emerges from learned distributed representations and weighted interactions among simple units.
Critique. Opacity, data/compute demands, unstable optimization, and weak guarantees or causal grounding.
Modern descendants. Foundation models, multimodal networks, representation learning, and differentiable agents.
Understand
Plain-language record, transferred from the reviewed source module.
Theory or experimental setup. Demonstrated learned local receptive fields, weight sharing, and end-to-end recognition in a practical deployment.
Result / historical claim. Performance depended on labeled data and narrow image distributions; compute limited depth and scale.
Apply
Professional implication, only where the reviewed record states one.
The checked-in record does not state a separate professional application for this entry. The topic page places it in the wider research lineage: .
Verify
Evidence status, stated limitations, and the external sources this record actually carries.
Evidence form. Trained convolutional networks end to end on handwritten digit recognition and integrated them into document-processing systems
Limitation / debate. Modern computer vision, spatial inductive bias, learned perception, and the MNIST benchmark culture.
Source status. This milestone row does not carry a primary-source URL in the approved export, and we do not have a verified link for it in our own research. We do not guess one.
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Reproduce
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Cite or share
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