Review of Feature Extraction Techniques for Character Recognition

Soora, Narasimha Reddy; Deshpande, Parag S.

doi:10.1080/03772063.2017.1351323

Cited by 27 publications

(7 citation statements)

References 65 publications

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“…Dalam bidang pengenalan karakter ada beberapa metode ekstraksi fitur yang dapat dilakukan. Soora membahas dalam penelitiannya membagi fitur menjadi 2 kelompok, yaitu fitur berbasis bentuk dan fitur tidak berbasis bentuk [2]. Salah satu fitur tidak berbasis bentuk adalah profil proyeksi yang merupakan fitur statistic.…”

Section: Pendahuluanunclassified

Histogram Profil Proyeksi sebagai Metode Ekstraksi Fitur pada Pengenalan Karakter Tulisan Tangan

Anggraeny

Munir²,

Purbasari

2020

santika

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Penelitian tentang pengenalan karakter masih sangat diminati sampai saat ini karena area aplikasinya yang luas. Topik penelitiannya dapat terkait dengan preprocessing, ekstraksi fitur dan metode pengenalan karakter. Dalam bidang pengenalan karakter ada beberapa metode ekstraksi fitur yang dapat digunakan, salah satunya profil proyeksi. Profil proyeksi adalah suatu histogram yang memproyeksikan piksel citra kearah horisontal maupun vertikal. Histogram proyeksi menghitung jumlah piksel non-background pada arah spesifik tertentu, horisontal dan vertikal. Dalam penelitian ini dilakukan analisa terhadap penggunakan proyeksi vertikal, horisontal, dan gabungan terhadap karakter huruf Roman yang diklasifikasi menggunakan k-NN. Hasil dari penelitian ini memberikan performa akurasi terbaik 80,95% dengan waktu yang dibutuhkan 0,66 detik.

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Section: Pendahuluanunclassified

Histogram Profil Proyeksi sebagai Metode Ekstraksi Fitur pada Pengenalan Karakter Tulisan Tangan

Anggraeny

Munir²,

Purbasari

2020

santika

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“…The vision training of computers occupied a great attention to develop intelligent tools for specific needs, such as the monitoring tools for security purposes, depending on the Fuzzy logic method [11]. The development of Fuzzy logic in control applications, as in the inverted pendulum engineering approach, was presented without the need to do high computations [12]. Handwriting English Numbers Recognition (HENR) has been used in computer vision with the real-time applications [9].…”

Section: Introductionmentioning

confidence: 99%

Feature Extraction in Six Blocks to Detect and Recognize English Numbers

Abbas

Mohamad

2021

eijs

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The Fuzzy Logic method was implemented to detect and recognize English numbers in this paper. The extracted features within this method make the detection easy and accurate. These features depend on the crossing point of two vertical lines with one horizontal line to be used from the Fuzzy logic method, as shown by the Matlab code in this study. The font types are Times New Roman, Arial, Calabria, Arabic, and Andalus with different font sizes of 10, 16, 22, 28, 36, 42, 50 and 72. These numbers are isolated automatically with the designed algorithm, for which the code is also presented. The number’s image is tested with the Fuzzy algorithm depending on six-block properties only. Groups of regions (High, Medium, and Low) for each number showed unique behavior to recognize any number. Normalized Absolute Error (NAE) equation was used to evaluate the error percentage for the suggested algorithm. The lowest error was 0.001% compared with the real number. The data were checked by the support vector machine (SVM) algorithm to confirm the quality and the efficiency of the suggested method, where the matching was found to be 100% between the data of the suggested method and SVM. The six properties offer a new method to build a rule-based feature extraction technique in different applications and detect any text recognition with a low computational cost.

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“…These features are used to train the model of a machine learning algorithm or classifier for character recognition. We evaluate a number of classifiers such as the support vector machine (SVM) [9,10], K-nearest neighbors (KNN) [11,12], artificial neural network (ANN) [13] and Decision Trees [14]. The main contributions of this paper are as follows: Development of a prototype for barrier access control and then deploying it in a real world scenario.An image dataset of challenging number plates commonly used in Pakistan with variations in background, position on vehicle, fonts and font styles.Development of an algorithm for character extraction and segmentation of LPs having different background, position on vehicle, fonts and font styles.An extensive performance evaluation of classifiers for optical character recognition.A performance evaluation of the proposed system on two different hardware environments to select the one which is favorable for real-time application.…”

Section: Introductionmentioning

confidence: 99%

Barrier Access Control Using Sensors Platform and Vehicle License Plate Characters Recognition

Ullah

Anwar

Shahzadi

et al. 2019

Sensors

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The paper proposes a sensors platform to control a barrier that is installed for vehicles entrance. This platform is automatized by image-based license plate recognition of the vehicle. However, in situations where standardized license plates are not used, such image-based recognition becomes non-trivial and challenging due to the variations in license plate background, fonts and deformations. The proposed method first detects the approaching vehicle via ultrasonic sensors and, at the same time, captures its image via a camera installed along with the barrier. From this image, the license plate is automatically extracted and further processed to segment the license plate characters. Finally, these characters are recognized with the help of a standard optical character recognition (OCR) pipeline. The evaluation of the proposed system shows an accuracy of 98% for license plates extraction, 96% for character segmentation and 93% for character recognition.

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Review of Feature Extraction Techniques for Character Recognition

Cited by 27 publications

References 65 publications

Histogram Profil Proyeksi sebagai Metode Ekstraksi Fitur pada Pengenalan Karakter Tulisan Tangan

Histogram Profil Proyeksi sebagai Metode Ekstraksi Fitur pada Pengenalan Karakter Tulisan Tangan

Feature Extraction in Six Blocks to Detect and Recognize English Numbers

Barrier Access Control Using Sensors Platform and Vehicle License Plate Characters Recognition

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