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Combinational neural network using Gabor filters for the classification of handwritten digits

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Paper breakdownThe authors' claims, conditions and measurements, as JSON Signed receiptProof this review has not been altered
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N. Joshi

Computer Vision and Pattern Recognition · Submitted 18 Sep 2017

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim With this method the computational time required for training the dataset has been reduced substancially.

how it is stated · asserted 1.00 read the record →

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arXiv ID1709.05867 (version 1)
Pith IDQ7QLWOQE, Pith's permanent record of this paper and its review
CitationN. Joshi. "Combinational neural network using Gabor filters for the classification of handwritten digits." arXiv:1709.05867 (2017). https://pith.science/paper/Q7QLWOQE
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The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 3,487 input tokens.

What the abstract itself states, as probabilities: claims first 0.03 · states limitation 0.10 · human participants 0.06 · controlled comparison 0.07 · randomized assignment 0.01 · states quantitative result 0.05 · compares with prior results 0.17 · mentions code or data availability 0.04.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

A classification algorithm that combines the components of k-nearest neighbours and multilayer neural networks has been designed and tested. With this method the computational time required for training the dataset has been reduced substancially. Gabor filters were used for the feature extraction to ensure a better performance. This algorithm is tested with MNIST dataset and it will be integrated as a module in the object recognition software which is currently under development.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 A classification algorithm that combines the components of k-nearest neighbours and multilayer neural networks has been designed and tested. contribution 0.85 0.92 0.27 asserted 1.00
s1 With this method the computational time required for training the dataset has been reduced substancially. result 1.00 0.89 0.19 asserted 1.00
s2 Gabor filters were used for the feature extraction to ensure a better performance. method 1.00 0.85 0.08 asserted 0.99
s3 This algorithm is tested with MNIST dataset and it will be integrated as a module in the object recognition software which is currently under development. future work 0.89 0.73 0.17 asserted 0.83

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from observation or experiment 0.69.

What was examined, and what was not

sources read
  • arXiv listing abstract v1
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

A classification algorithm that combines the components of k-nearest neighbours and multilayer neural networks has been designed and tested. With this method the computational time required for training the dataset has been reduced substancially. Gabor filters were used for the feature extraction to ensure a better performance. This algorithm is tested with MNIST dataset and it will be integrated as a module in the object recognition software which is currently under development.

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