A kNN-based approach for the machine vision of character recognition of license plate numbers

Quiros, Ana Riza F.; Bedruz, Rhen Anjerome; Uy, Aaron Christian; Abad, Alexander C.; Dadios, Elmer P.; Fernando, Arvin H.

doi:10.1109/tencon.2017.8228018

Cited by 37 publications

(22 citation statements)

References 19 publications

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“…In the case of k > 1, a voting of majority decision is made to determine the class of the unknown sample. However, it can be seen from [5], [14], [17] that k = 1 consistently yields the highest accuracy and was verified by experimentation through the development process. Training the KNN classifier means saving the feature vectors from the training samples as opposed to other classifiers such as Support Vector Machines (SVM) and Artificial Neural Networks (ANN) in which parameters adapt or learn from the training samples.…”

Section: K-nearest Neighbor (Knn) As Character Classifiermentioning

confidence: 88%

“…KNN is an effective and widely used classifier in the industry despite is simplicity [4], [5]. It also has high fault tolerance over non-linear multiclass problems because it does not assume any models for the distribution of feature vectors in space [5], [8], [14]. KNN computes the distance from an unknown to all samples in the template space and remembers the minimum distance [5], [8], [14], [17].…”

Section: K-nearest Neighbor (Knn) As Character Classifiermentioning

confidence: 99%

“…It also has high fault tolerance over non-linear multiclass problems because it does not assume any models for the distribution of feature vectors in space [5], [8], [14]. KNN computes the distance from an unknown to all samples in the template space and remembers the minimum distance [5], [8], [14], [17]. The class that has the minimum distance from the unknown is the nearest neighbor k = 1 where the value k refers to the neighborhood size (Fig.…”

Section: K-nearest Neighbor (Knn) As Character Classifiermentioning

confidence: 99%

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Development of License Plate Recognition on Complex Scene with Plate-Style Classification and Confidence Scoring Based on KNN

Montero

Jeong

2018

IEICE Trans. Inf. & Syst.

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This paper presents an approach for developing an algorithm for automatic license plate recognition system (ALPR) on complex scenes. A plate-style classification method is also proposed in this paper to address the inherent challenges for ALPR in a system that uses multiple plate-styles (e.g., different fonts, multiple plate layout , variations in character sequences) which is the case in the current Philippine license plate system. Methods are proposed for each ALPR module: plate detection, character segmentation, and character recognition. K-nearest neighbor (KNN) is used as a classifier for character recognition together with a proposed confidence scoring to rate the decision made by the classifier. A small dataset of Philippine license plates but with relevant features of complex scenarios for ALPR is prepared. Using the proposed system on the prepared dataset, the performance of the system is evaluated on different categories of complex scenes. The proposed algorithm structure shows promising results and yielded an overall accuracy higher than the existing ALPR systems on the dataset consisting mostly of complex scenes.

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Section: K-nearest Neighbor (Knn) As Character Classifiermentioning

confidence: 88%

Section: K-nearest Neighbor (Knn) As Character Classifiermentioning

confidence: 99%

Section: K-nearest Neighbor (Knn) As Character Classifiermentioning

confidence: 99%

See 1 more Smart Citation

Development of License Plate Recognition on Complex Scene with Plate-Style Classification and Confidence Scoring Based on KNN

Montero

Jeong

2018

IEICE Trans. Inf. & Syst.

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“…Objek yang digunakan dalam penelitian rekognisi bervariasi, misalnya rel kereta [1], ekspresi wajah [2], plat nomor [3], tulisan tangan [4], dan tanda intonasi musik [5]. Metode yang digunakan pun beragam, misalnya deteksi tepi [6], knn [7] dan svm [8]. Metode tersebut memerlukan sebuah fitur dari objek, misalnya tekstur, warna dan bentuk.…”

Section: Pendahuluanunclassified

Model CNN LeNet dalam Rekognisi Angka Tahun pada Prasasti Peninggalan Kerajaan Majapahit

Septianto

Setyati

Santoso

2018

Jurnal Teknologi dan Sistem Komputer

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“…k-Nearest Neighbor (k-NN) algorithm is one of the simplest classification algorithms and it is one of the most used learning algorithms [19][20]. k-NN predictions are based on the intuitive assumption that objects close in distance are potentially similar, it makes good sense to discriminate between the K nearest neighbors when making predictions [21].…”

Section: K-nearest Neighbor Algorithm Modelmentioning

confidence: 99%

COMPRESSIVE STRENGTH MODELLING OF CONCRETE MIXED WITH FLY ASH AND WASTE CERAMICS USING k-NEAREST NEIGHBOR ALGORITHM

Elevado¹,

Galupino²,

Gallardo³

2018

GEOMATE

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Excessive materials are being manufactured, and along with it are the waste products that are being produced due to the rapid growth of industries. In the Philippines, wastes such as fly ash and damaged ceramics are being considered as a construction material since there are recent researches that proved their properties are comparable to cement and aggregates. In this study, compressive strength tests (ASTM C 39) were conducted to obtain the compressive strength of the concrete mixed with varying amounts fly ash and waste ceramics. Moreover, specimens were also subjected to varying days of curing to assess their strength development. Due to the availability of a wide range of data, machine learning model, such as the k-nearest neighbor, were also considered; it can predict an unknown target parameter without consuming tremendous time and resources. Thus, this study aimed to provide a k-nearest neighbor model that will serve as a reference to predict the compressive strength of concrete while incorporating waste ceramic tiles as a replacement to coarse aggregates while varying the amount of fly ash as a partial substitute to cement. The knearest neighbor model used was validated to ensure the predictions are acceptable.

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A kNN-based approach for the machine vision of character recognition of license plate numbers

Cited by 37 publications

References 19 publications

Development of License Plate Recognition on Complex Scene with Plate-Style Classification and Confidence Scoring Based on KNN

Development of License Plate Recognition on Complex Scene with Plate-Style Classification and Confidence Scoring Based on KNN

Model CNN LeNet dalam Rekognisi Angka Tahun pada Prasasti Peninggalan Kerajaan Majapahit

COMPRESSIVE STRENGTH MODELLING OF CONCRETE MIXED WITH FLY ASH AND WASTE CERAMICS USING k-NEAREST NEIGHBOR ALGORITHM

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