Farsi/Arabic handwritten digit recognition using quantum neural networks and bag of visual words method

Montazer, Gholam Ali; Soltanshahi, Mohammad Ali; Giveki, Davar

doi:10.3103/s1060992x17020060

Cited by 12 publications

(3 citation statements)

References 19 publications

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“…The 99.07% of accuracy for HODA dataset was obtained. Montazer et al [ 16 ] developed a new approach for handwritten digit recognition. They applied Bag of Visual Words (BoVW) technique and constructed images feature vectors in which each visual word is described by Scale Invariant Feature Transform (SIFT) method and Quantum Neural Networks (QNN) used as classifier.…”

Section: ) Literature Reviewmentioning

confidence: 99%

“…Dataset Accuracy Alaei [32] HODA 99.37% Hosseini [33] HODA 97.12% Parseh [11] HODA 99.07% Montazer [16] HODA 99.30% Al-wajih [34] HODA 99.13% Sajedi [15] HODA 99.07% Khorashadizadeh [2] HODA 99.58% Bossaghzadeh [24] HODA 99.69% Nanehkaran [18] HODA 99.45% Safarzadeh [35] HODA 99.37% Parseh [17] HODA 99.56% HODA 88.9 % Ghods [13] TMU 95.9% Sarvaramini [21] HODA 97.7% Roohi [22] HODA 97.1% For digit data, confusion matrixes have also been provided for HODA and Sadri dataset,…”

Section: Referencementioning

confidence: 99%

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Persian handwritten digit, character and word recognition using deep learning

Bonyani

Jahangard

Daneshmand

2021

IJDAR

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Digit, character, and word recognition of a particular script play a key role in the field of pattern recognition. These days, Optical Character Recognition (OCR) systems are widely used in commercial market in various applications. In recent years, there are intensive research studies on optical character, digit, and word recognition. However, only a limited number of works are offered for numeral, character, and word recognition of Persian scripts. In this paper, we have used deep neural network and investigated different versions of DensNet models and Xception and compare our results with the state-of-the-art methods and approaches in recognizing Persian character, number, and word. Two holistic Persian handwritten datasets, HODA and Sadri, have been used. For a comparison of our proposed deep neural network with previously published research studies, the best state-of-the-art results have been considered. We used accuracy as our criteria for evaluation. For HODA dataset, we achieved 99.72% and 89.99% for digit and character, respectively. For Sadri dataset, we obtained accuracy rates of 99.72%, 98.32%, and 98.82% for digit, character, and words, respectively.

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Section: ) Literature Reviewmentioning

confidence: 99%

Section: Referencementioning

confidence: 99%

Persian handwritten digit, character and word recognition using deep learning

Bonyani

Jahangard

Daneshmand

2021

IJDAR

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“…The massive amount of data available on the internet has attracted many researchers to work on various fields such as computer vision (Giveki et al 2017 ; Montazer et al 2017 ), transfer learning (Giveki et al 2022 ), data science (Mosaddegh et al 2021 ; Soltanshahi et al 2022 ), social networks (Ahmadi et al 2020 ), knowledge graph (Molaei et al 2020 ). Knowledge graphs have many applications in fields such as health (Li et al 2020 ), finance (Huakui et al 2020 ), education (Shi et al 2020 ), cyberspace security (Zhang and Liu 2020 ), social networks (Zou 2020 ).…”

Section: Introductionmentioning

confidence: 99%

BuB: a builder-booster model for link prediction on knowledge graphs

2023

Self Cite

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Link prediction (LP) has many applications in various fields. Much research has been carried out on the LP field, and one of the most critical problems in LP models is handling one-to-many and many-to-many relationships. To the best of our knowledge, there is no research on discriminative fine-tuning (DFT). DFT means having different learning rates for every parts of the model. We introduce the BuB model, which has two parts: relationship Builder and Relationship Booster. Relationship Builder is responsible for building the relationship, and Relationship Booster is responsible for strengthening the relationship. By writing the ranking function in polar coordinates and using the nth root, our proposed method provides solutions for handling one-to-many and many-to-many relationships and increases the optimal solutions space. We try to increase the importance of the Builder part by controlling the learning rate using the DFT concept. The experimental results show that the proposed method outperforms state-of-the-art methods on benchmark datasets.

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An enhanced LBP-based technique with various size of sliding window approach for handwritten Arabic digit recognition

Al-wajih

Ghazali

2021

Multimed Tools Appl

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Farsi/Arabic handwritten digit recognition using quantum neural networks and bag of visual words method

Cited by 12 publications

References 19 publications

Persian handwritten digit, character and word recognition using deep learning

Persian handwritten digit, character and word recognition using deep learning

BuB: a builder-booster model for link prediction on knowledge graphs

An enhanced LBP-based technique with various size of sliding window approach for handwritten Arabic digit recognition

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