Handwriting Word Recognition Based on SVM Classifier

Kadhm, Mustafa Salam; Hassan, Asst. Prof. Dr. Alia Karim Abdul

doi:10.14569/ijacsa.2015.061109

Cited by 6 publications

(5 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Support Vector Machine adalah metode yang pengelompokkan citra yang menggunakan konsep dasar menggunakan fungsi linier yang memisahkan data pelatihan menjadi dua kelas dengan memaksimalkan margin. SVM adalah klasifikasi biner, yang mengategorikan data menjadi dua kelas dan merupakan model pembelajaran yang diawasi [15]- [17]. SVM merupakan salah satu metode klasifikasi yang baik untuk memecahkan masalah dua kelas, sehingga penelitian lebih lanjut perihal kasus multiclass SVM terus dikembangkan [11].…”

Section: Support Vector Machine (Svm)unclassified

Multi-SVM Dalam Identifikasi Bunga Berbasis Ekstraksi Ciri Orde Satu

Sari

2023

InComTech

View full text Add to dashboard Cite

Bunga merupakan modifikasi tunas dimana bentuk, warna, dan susunannya menyesuaikan kepentingan dari tumbuhan tersebut. Bunga berfungsi sebagai tempat berlangsungnya penyerbukan. Ada sangat banyak jenis bunga yang dapat dikenali di dunia ini. Perkembangan teknologi saat ini dapat dimanfaatkan sebagai fasilitas kepada manusia untuk membangun sebuah sistem yang dapat mengenali suatu citra. Penelitian ini, mengusulkan teknik mengidentifikasi citra bunga dengan ekstraksi ciri orde satu dan berbasis Multi-Support Vector Machine (Multi-SVM). Pemilihan ekstraksi ciri orde satu adalah karena merupakan ekstraksi ciri tekstur pada struktur makro yang dianggap cocok dalam mengidentifikasi jenis bunga. Pada tahap ekstraksi ciri, citra yang semula adalah citra RGB dikonversi terlebih dahulu menjadi citra berskala abu-abu. Multi-SVM memiliki keunggulan dalam mengklasifikasikan lebih dari dua kelas. Dalam penelitian ini digunakan lima jenis bunga yaitu Rose, Calendula, Peony, Leucanthemum Maximum, dan Iris dengan 300 citra pelatihan dan 150 citra pengujian. Berdasarkan pengujian identifikasi, menghasilkam akurasi sebesar 90.6667%.

show abstract

Section: Support Vector Machine (Svm)unclassified

Multi-SVM Dalam Identifikasi Bunga Berbasis Ekstraksi Ciri Orde Satu

Sari

2023

InComTech

View full text Add to dashboard Cite

show abstract

“…Most of the time, the relevance scores between each feature and the class vector are calculated, as well as the highest-scoring features are selected. Filtering techniques are basic, quick, and simple to comprehend [41] [42]. They do not, however, take into account redundancy or the interaction of characteristics; instead, they believe these features are unrelated.…”

Section: Techniques For Feature Selectionmentioning

confidence: 99%

Predicting Alzheimer's Disease Using Filter Feature Selection Method

2022

IJCCCE

View full text Add to dashboard Cite

Alzheimer’s disease (AD) is caused by multiple variables. Alzheimer's disease development and progression are influenced by genetic variants. The molecular pathways causing Alzheimer's disease are still poorly understood. In Alzheimer's disease research, determining an effective and reliable diagnosis remains a major difficulty, particularly in the early stages (i.e., Moderate Cognitive Impairment (MCI)). Researchers and technologists working in the fields of machine learning and data mining can help improve the situation, early AD diagnosis but face a hurdle when it comes to high-dimensional data processing. By reducing irrelevant and redundant data from microarray gene expression data, the technique of feature selection can save computing time, improve learning accuracy, and encourage a deeper effect on the learning system or data. The feature selection strategy described in this article reduces data noise well. In particular, Pearson's correlation coefficient is used to assess data redundancy. The efficacy of these features is assessed using the Support Vector Machine (SVM) classification approach. The proposed approach has an accuracy of up to 91.1 %. As a result, newly established approaches for early diagnosis of Alzheimer's disease(AD) are being improved. Index Terms— Alzheimer’s Disease, Support vector machine, machine learning, feature selection, Pearson’s correlation coefficient.

show abstract

“…By implementing these two preliminary steps a better recognition is obtained comparing with robert and sobel's filters [22]. The estimated value of the vertical and horizontal at each point for directions and gradients is presented in (1) below to obtain the approximated value for the norm of the gradient:…”

Section: Histogram Of Oriented Gradient (Hog)mentioning

confidence: 99%

“…It is also necessary to distinguish online recognition (on-line) writing manuscript, which had the character of the interface between man and computer (a special pen connected to the machine and only works on a sensitive tablet), recognition offline (off-line). Only the offline recognition will be considered in this work [1], [2].…”

Section: Introductionmentioning

confidence: 99%

Isolated Arabic handwritten words recognition using EHD and HOG methods

Mohammed¹,

Tariq²,

Ayad

2021

IJEECS

View full text Add to dashboard Cite

<span>Handwriting recognition is a growing field of study in computer vision, artificial intelligence and pattern recognition technology aimed to recognizing texts and handwritings of hefty amount of produced official documents and paper works by institutes or governments. Using computer to distinguish and make these documents accessible and approachable is the goal of these efforts. Moreover, recognition of text has accomplished practically a major progress in many domains such as security sector and e-government structure and more. A system for recognition text’s handwriting was presented here relied on edge histogram descriptor (EHD), histogram of orientated gradients (HOG) features extraction and support vector machine (SVM) as a classifier is proposed in this paper. HOG and EHD give an optimal features of the Arabic hand-written text by extracting the directional properties of the text. Besides that, SVM is a most common machine learning classifier that obtaining an essential classification results within various kernel functions. The experimental evaluation is carried out for Arabic handwritten images from IESK-ArDB database using HOG, EHD features and proposed work provides 85% recognition rate.</span>

show abstract

Handwriting Word Recognition Based on SVM Classifier

Cited by 6 publications

References 14 publications

Multi-SVM Dalam Identifikasi Bunga Berbasis Ekstraksi Ciri Orde Satu

Multi-SVM Dalam Identifikasi Bunga Berbasis Ekstraksi Ciri Orde Satu

Predicting Alzheimer's Disease Using Filter Feature Selection Method

Isolated Arabic handwritten words recognition using EHD and HOG methods

Contact Info

Product

Resources

About