Gradient-based learning applied to document recognition
Open accessAbstract
Multilayer neural networks trained with the back-propagation algorithm constitute the best example of a successful gradient based learning technique. Given an appropriate network architecture, gradient-based learning algorithms can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters, with minimal preprocessing. This paper reviews various methods applied to handwritten character recognition and compares them on a standard handwritten digit recognition task. Convolutional neural networks, which are specifically designed to deal with the variability of 2D shapes, are shown to outperform all other techniques. Real-life document recognition systems are composed of multiple modules including field extraction, segmentation recognition, and language modeling. A new learning paradigm, called graph transformer networks (GTN), allows such multimodule systems to be trained globally using gradient-based methods so as to minimize an overall performance measure. Two systems for online handwriting recognition are described. Experiments demonstrate the advantage of global training, and the flexibility of graph transformer networks. A graph transformer network for reading a bank cheque is also described. It uses convolutional neural network character recognizers combined with global training techniques to provide record accuracy on business and personal cheques. It is deployed commercially and reads several million cheques per day.
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The use of pooling leads to loss of the exact position of the segmented voxel and thus can negatively impact accuracy.
“An alternative would be the use of pooling ( LeCun et al., 1998 ), which however leads to loss of the exact position of the segmented voxel and thus can negatively impact accuracy.”
From Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation (2017) · cites this paper as LeCun et al., 1998
In “A. Convolutional Networks” (page unavailable), the paper says subsampling reduces the precision with which feature positions are encoded. It discusses character recognition, not segmented voxels, and presents reduced positional precision as improving robustness to shifts and distortions rather than as a cause of lower accuracy. (LLM, conf=0.88)
Experimental — accuracy not yet measuredFeed-forward deep networks, in particular convolutional networks, are well-known to provide state-of-the-art results for image classification.
“For image classification feed-forward deep networks, and in particular convolutional networks, are well-known to provide state-of-the-art results [26, 25].”
From Communication-Efficient Learning of Deep Networks from Decentralized Data (2016) · cites this paper as [26]
The paper’s “Learning in Real Handwriting Recognition Systems” section reports that convolutional networks performed better than the other methods tested on handwritten-digit recognition. That supports the claim for a specific image-classification task, but not its broader assertion that feed-forward deep networks generally provide state-of-the-art image-classification results. No page number is available in our copy. (LLM, conf=0.86)
Experimental — accuracy not yet measuredObject detectors based on CNN can be categorized into two primary types: single/one-stage detectors and two-stage detectors.
“Overall, object detectors based on CNN [87] can be categorized into two primary types.”
From Object Detection in Autonomous Vehicles under Adverse Weather: A Review of Traditional and Deep Learning Approaches (2024) · cites this paper as [87]
The section “Object Detection and Spotting with SDNN” describes using a convolutional network for object detection, supporting the CNN-based detection part of the claim. It does not categorize detectors into one-stage and two-stage types. (LLM, conf=0.88)
Experimental — accuracy not yet measuredThe normalization method makes the network robust to illumination and contrast variations.
“The normalization makes the network robust to illumination and contrast variation as demonstrated by [20].”
From Multimodal learning for facial expression recognition (2015) · cites this paper as [20]
The Object Detection and Spotting with SDNN section (page unavailable) says a Laplacian filter removes global illumination variations before face images are size-normalized. It does not attribute that effect to normalization or demonstrate robustness to contrast variation; the Invariance and Noise Resistance section instead discusses robustness to geometric distortions and noise. (LLM, conf=0.77)
Experimental — accuracy not yet measuredLifelong learning enables the prediction model to be updated in an online way, thereby maintaining its prediction performance.
“In the edge-assisted SLAM system, since video frames and the corresponding tiles are generated on the fly, lifelong learning enables the prediction model to be updated in an online way, thereby maintaining its prediction performance.”
From Orchestrating Joint Offloading and Scheduling for Low-Latency Edge SLAM (2025) · cites this paper as [27]
In “B. Gradient-Based Learning” (page unavailable), the paper describes stochastic gradient as an “online update” that changes model parameters using individual samples. It does not address lifelong learning in an edge-assisted SLAM system or show that such updates maintain prediction performance as frames and tiles arrive. (LLM, conf=0.86)
Experimental — accuracy not yet measured
Eigen et al. proposed a system to remove dirt and raindrop noise from a single image using the NN architecture originally developed by LeCun et al.
“Eigen et al. [46] proposed a system to remove dirt and raindrop noise from a single image, using the NN architecture originally developed by LeCun et al. [47].”
From A Review of Detection and Removal of Raindrops in Automotive Vision Systems (2021) · cites this paper as [47]
The mapping method is highly efficient in hardware usage and causes only moderate accuracy degradation compared to software-based neural networks on MNIST and CIFAR-10.
“This method, which we describe in more detail in Supplementary Note 4, is highly efficient in terms of hardware usage and causes only moderate accuracy degradation compared to software-based neural networks on the two tasks considered here: Modified National Institute of Standards and Technology (MNIST) handwritten digit recognition30 and CIFAR-10 image recognition31.”
From Powering AI at the edge: A robust, memristor-based binarized neural network with near-memory computing and miniaturized solar cell (2024) · cites this paper as [30]
Existing reviews in this context mostly comprise CNN and LSTM, leaving behind relevant and novel architectures such as Autoencoders, CapsNet, Deep Belief Networks, and GANs.
“Moreover, even though there are reviews in this context [2], most of the surveyed works comprise Convolutional Neural Networks (CNN) [49] and Long short-term memory (LSTM) [32, 55], leaving behind some relevant and novel architectures, such as Autoencoders [108], Capsule Networks (CapsNet) [82], Deep Belief Networks [31], and Generative Adversarial Networks (GANs) [23].”
From Gait Recognition Based on Deep Learning: A Survey (2023) · cites this paper as [49]
Shiraga et al. used an architecture similar to LeNet to create a GEI-based recognizer, achieving 91.5% accuracy on the OU-ISIR Large Population dataset.
“In the context of gait recognition, Shiraga et al. [86] used an architecture similar to LENet [49] to create a gleaming-based recognizer through Gait Energy Image (GEI). The model achieved an accuracy of 91 . 5% in the OU-ISIR [59] Large Population dataset.”
From Gait Recognition Based on Deep Learning: A Survey (2023) · cites this paper as [49]
The NeuRRAM chip achieves 1.6-times to 2.3-times lower EDP and 7-times to 13-times higher computational density at various MVM input and output bit-precisions than previous state-of-the-art RRAM-based CIM chips, despite being fabricated at an older technology node.
“the NeuRRAM chip achieves 1.6-times to 2.3-times lower EDP and 7-times to 13-times higher computational density (measured by throughput per million of RRAMs) at various MVM input and output bit-precisions than previous state-of-the-art RRAM-based CIM chips, despite being fabricated at an older technology node17–27,36.”
From A compute-in-memory chip based on resistive random-access memory (2022) · cites this paper as [18]
The Discussion passage describes hardware implementations of convolutional networks, but does not address NeuRRAM or comparisons with RRAM-based compute-in-memory chips; no page number is available in our copy. Searches for NeuRRAM, EDP, computational density, bit precision, and technology node found no passage supporting the claimed figures. (LLM, conf=0.78)
Experimental — accuracy not yet measuredViT was designed for image classification specifically.
“Unlike the recentlyproposed Vision Transformer (ViT) that was designed for image classification specifically”
From Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions (2021) · cites this paper as [13]
The supplied passages discuss document and character recognition, not Vision Transformer (ViT). Searches for ViT and transformer-based image classification returned passages on this paper’s recognition and Viterbi transformers (section B, page unavailable), but nothing about ViT’s design purpose. (LLM, conf=0.78)
Experimental — accuracy not yet measuredViT has a columnar structure with coarse image patches as input.
“ViT has a columnar structure with coarse image patches as input.”
From Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions (2021) · cites this paper as [13]
The Convolutional Networks section (page unavailable) describes LeNet-5’s character-image inputs, local receptive fields, and subsampling, not ViT. A further search for ViT, image patches, and transformer inputs found no passage addressing the claimed architecture. (LLM, conf=0.76)
Experimental — accuracy not yet measuredViT is applicable to image classification.
“Although ViT is applicable to image classification”
From Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions (2021) · cites this paper as [13]
The Recognition Transformer and Viterbi Transformer section describes graph-based document recognition, not ViT. A search for ViT, vision transformers, and image classification found no passage in our copy addressing whether ViT is applicable to image classification. (LLM, conf=0.75)
Experimental — accuracy not yet measuredViT's output feature map is single-scale and low-resolution.
“its output feature map is single-scale and low-resolution”
From Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions (2021) · cites this paper as [13]
The Convolutional Networks and LeNet-5 sections discuss how subsampling reduces the resolution of LeNet-5 feature maps, not the output of ViT. Searches for ViT, vision transformers, and single-scale low-resolution output found no passage addressing the attributed claim; page numbers were not provided. (LLM, conf=0.77)
Experimental — accuracy not yet measuredViT is a columnar framework whose output is single-scale.
“ViT (see Figure 1 (b)) is a columnar framework, whose output is single-scale.”
From Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions (2021) · cites this paper as [13]
The Convolutional Networks and LeNet-5 sections describe convolutional feature maps and changing spatial resolution, not ViT. Searches for “ViT columnar framework single-scale” and “vision transformer single-scale output columnar architecture” found no passage in our copy addressing ViT’s architecture or output scale. (LLM, conf=0.76)
Experimental — accuracy not yet measuredPVT obtains 40.4 AP on COCO val2017, 8.7 points higher than ViT-Small/32's 31.7.
“Thus, it obtains a promising AP of 40.4 on COCO val2017 , 8.7 points higher than ViT-Small/32 (40.4 vs . 31.7).”
From Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions (2021) · cites this paper as [13]
Our copy reports character-recognition error rates in “Comparison with Other Classifiers” and discusses other object-detection methods in “Object Detection and Spotting with SDNN” (pages unavailable). Searches for PVT, COCO val2017, ViT-Small/32, and the stated AP values found no passage addressing the comparison. (LLM, conf=0.78)
Experimental — accuracy not yet measuredWith increasing input scale, the growth rate of the GFLOPs of PVT is greater than ResNet but lower than ViT.
“With increasing input scale, the growth rate of the GFLOPs of our PVT is greater than ResNet [22], but lower than ViT [13], as shown in Figure 6.”
From Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions (2021) · cites this paper as [13]
The Discussion compares multiply-accumulate operations for recognizing a single size-normalized image, and Section VII discusses computational savings when convolutional networks process larger input fields. Neither passage addresses ResNet or compares its GFLOPs growth with PVT as input scale increases. Searches for ResNet, GFLOPs, and input-resolution complexity found no such comparison in our copy. (LLM, conf=0.78)
Experimental — accuracy not yet measuredViT and DeiT have limitations for dense prediction tasks because they are specifically designed for classification and are not suitable for dense prediction tasks, which usually require effective feature pyramids.
“Note that ViT and DeiT have limitations as they are specifically designed for classification tasks, and thus are not suitable for dense prediction tasks, which usually require effective feature pyramids.”
From Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions (2021) · cites this paper as [13]
The passages in “Convolutional Networks” discuss LeNet-5 feature maps and subsampling, not ViT or DeiT. A search for ViT, DeiT, dense prediction, and feature pyramids found no passage in our copy addressing the attributed claim; page numbers were not available. (LLM, conf=0.78)
Experimental — accuracy not yet measuredDCNNs have brought about breakthroughs in processing images, video, speech and audio.
“Among different types of deep neural networks, DCNNs (LeCun et al. 1998(LeCun et al. , 2015;;Krizhevsky et al. 2012a) have brought about breakthroughs in processing images, video, speech and audio.”
From Deep Learning for Generic Object Detection: A Survey (2020) · cites this paper as LeCun et al. 1998
The CNN was first proposed and advocated for high-dimensional image analysis by Lecun et al.
“The CNN was first proposed and advocated for the high-dimensional image analysis by Lecun et al. 38”
From Artificial intelligence in healthcare: past, present and future (2017) · cites this paper as [38]
In the early days of modern deep learning one tended to use very simple combinations of the building blocks, as in Lenet and AlexNet.
“In the early days of modern deep learning one tended to use very simple combinations of the building blocks, as in Lenet [57] and AlexNet [1].”
From An overview of deep learning in medical imaging focusing on MRI (2019) · cites this paper as [57]
In medical imaging the interest in deep learning is mostly triggered by convolutional neural networks (CNNs), a powerful way to learn useful representations of images and other structured data.
“In medical imaging the interest in deep learning is mostly triggered by convolutional neural networks (CNNs) [57] 15 , a powerful way to learn useful representations of images and other structured data.”
From An overview of deep learning in medical imaging focusing on MRI (2019) · cites this paper as [57]
Recent studies have shown that deep learning techniques are successfully applied to the medical domain.
“Recent studies have shown that deep learning techniques are successfully applied to medical domain.”
From Medical image retrieval using deep convolutional neural network (2017) · cites this paper as [40]
Convolutional Neural Networks are inspired by the hierarchical process of simple and complex cells in the human brain to extract and learn different information from visual stimuli.
“Convolutional Neural Networks (CNN) were introduced formally by Lecun, Bottou, Bengio, and Haffner (1998).They are inspired by the hierarchical process of simple and complex cells in the human brain to extract and learn different information from visual stimuli.”
From Multimodal emotional state recognition using sequence-dependent deep hierarchical features (2015) · cites this paper as Lecun, Bottou, Bengio, and Haffner (1998)
Most of the datasets in Table 7 are the datasets for image classification, such as MNIST, CIFAR-10, ImageNet, Fashion-MNIST, SVHN, and IMDB-WIKI dataset.
“Since most of the studies are focused on image classification problems, most of the datasets in Table 7 are the datasets for image classification, such as MNIST (LeCun et al., 1998), CIFAR-10 ( Krizhevsky and Hinton, 2009), ImageNet (Fei-Fei, 2010), Fashion-MNIST (Xiao et al., 2017), SVHN (Netzer et al., 2011), and IMDB-WIKI dataset.”
From A software engineering perspective on engineering machine learning systems: State of the art and challenges (2021) · cites this paper as LeCun et al., 1998
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- Authors
- Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P.
- Affiliations
- AT&T (United States) (US)
- Université de Montréal (CA)
- Venue
- Proceedings of the IEEE
- Published
- 1998
- Volume
- 86
- Issue
- 11
- Pages
- 2278-2324
- ISSN
- 0018-9219 (ISSN-L)
- OpenAlex
- W2112796928
- Semantic Scholar
- 162d958ff885f1462aeda91cd72582323fd6a1f4
- Cited by
- 58,805
- References
- 125
- Citations by year
- 2026: 3932 · 2025: 6037 · 2024: 5859 · 2023: 5924 · 2022: 5751 · 2021: 6779 · 2020: 6359 · 2019: 5708 · 2018: 4169 · 2017: 2778 · 2016: 1818 · 2015: 1223 · 2014: 644 · 2013: 396 · 2012: 267
- FWCI
- 12.51
- Type
- journal article
- Language
- en
- Open access
- green
- hal.science
- Locations
- Proceedings of the IEEE (publishedVersion)
- HAL (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Handwritten Text Recognition Techniques
- Physical Sciences › Engineering › Media Technology › Vehicle License Plate Recognition
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Keywords
- document recognition, handwritten character recognition, handwritten digit recognition, convolutional neural networks, graph transformer networks, global training, backpropagation, online handwriting recognition, field extraction, language modeling
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