Meme Opinion Categorization by Using Optical Character Recognition (OCR) and Naïve Bayes Algorithm

Amalia, Amalia; Sharif, Arner; Haisar, Fikri; Gunawan, Dani; Nasution, Benny Benyamin

doi:10.1109/iac.2018.8780410

Cited by 16 publications

(8 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Another example of traditional OCR approach is implemented using Radial Basis Function (RBF) [12]. Random forests [13] and Naive Bayes [14] algorithm are also evaluated for optical character recognition. All the aforementioned approaches involved aspect of classification, which wouldn't have resulted in success if not for intelligent feature description.…”

Section: Literature Reviewmentioning

confidence: 99%

Evaluation of deep learning approaches for optical character recognition in Urdu language

Riaz

Monir

Hasan

2022

Mehran Univ. res. j. eng. technol.

View full text Add to dashboard Cite

With the evolving technological era, the optical character recognition systems have substantial execution, considering the widespread use of daily hand-written human transaction. Optical Character Recognition (OCR) is an implementation of Computer Vision that digitizes numerous hand dealt documents for further analysis and formatting. OCR is achieved by various ways such as discriminative analysis and deep learning. This paper focuses on evaluating deep learning models on a hand-written compiled dataset of Urdu digits. The evaluation is performed for deep convolutional learning algorithms; VGGNet16, InceptionV3, ResNet50, and DenseNet121. The convolutional models are pre-trained on the ImageNet. The model weights of fully connected layers have been evaluated, reducing the training time of the convolutional layers. The testing accuracy for the compiled dataset is observed as, ResNet50 with 96%, InceptionV3 with 95%, VGGNet16 with 95% and DenseNet121 with 94%.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Evaluation of deep learning approaches for optical character recognition in Urdu language

Riaz

Monir

Hasan

2022

Mehran Univ. res. j. eng. technol.

View full text Add to dashboard Cite

show abstract

“…Ekstraksi informasi merupakan salah satu bagian dari Pemrosesan Bahasa Alami (Natural Language Processing). Pada penelitian ini proses ektraksi akan dilakukan dengan menggunakan metode Optical Character Recognition (OCR), dimana proses ini dilakukan untuk mengubah pada bagian teks dari sebuah citra optis yang memiliki dokumen atau teks didalamnya (Amalia, Sharif, Haisar, Gunawan, & Nasution, 2018). Menurut Cherriet, OCR adalah sebuah aplikasi komputer yang digunakan untuk mengidentifikasi citra huruf maupun angka untuk dikonversi ke dalam bentuk yang dapat diubah.…”

Section: Pendahuluanunclassified

Ekstraksi Informasi Meme-Internet Berbahasa Indonesia Dengan Mesin Pencarian

Kristianto

2020

jist

View full text Add to dashboard Cite

Citra meme yang ada di internet memiliki informasi yang dapat digali untuk mendapatkan informasi baru yang diperlukan masyarakat. Ekstraksi Informasi merupakan salah satu ilmu dalam mengenali data teks tidak terstruktur menjadi data teks terstruktur. Dalam penelitian akan melakukan ujicoba dalam mengenali citra latar dan teks yang terkandung di dalam citra. Posisi teks yang hendak dikenali berada pada bagian atas dan bawah citra meme. Algoritma SIFT adalah salah satu metode ekstraksi fitur yang paling banyak digunakan dalam mengenali lokal fitur dari sebuah citra. Penggunaan algoritma SIFT dalam mengenali lokal fitur dari citra diharapkan dapat memberikan informasi yang baik. Sedangkan untuk kemiripan secara tekstual digunakan algoritma Jaro-Winkler distance. Uji coba dalam pra-proses dalam pembentukan database fitur citra dan kata menggunakan 1000 data citra hasil proses crawling dapat dilaksanakan. Praproses terutama untuk tahapan ekstraksi teks yang terdapat pada citra dapat berjalan baik. Pada tahap uji coba temu kembali informasi data citra yang digunakan sebanyak 50 citra. Hasil uji coba temu kembali informasi dengan memanfaatkaan database fitur yang terbentuk dalam penelitian, belum berjalan maksimal karena luaran dari sistem dalam memberikan hasil masih jauh dari hipotesa awal. Prosentase kemiripan citra hasil ujicoba luaran query masih di bawah 50%, terutama di bagian kesesuaian antara teks citra dan citra latar.

show abstract

“…Williams et al (2016) studied the racial microaggressions and perceptions of Internet memes. A few researchers (Amalia et al, 2018;Verma et al, 2020) have tried to distill the inherent sentiment of the meme contents.…”

Section: Related Workmentioning

confidence: 99%

CSECU_KDE_MA at SemEval-2020 Task 8: A Neural Attention Model for Memotion Analysis

Chy

Siddiqua

Aono

2020

Proceedings of the Fourteenth Workshop on Semantic Evaluation

View full text Add to dashboard Cite

A meme is a pictorial representation of an idea or theme. In the age of emerging volume of social media platforms, memes are spreading rapidly from person to person and becoming a trending ways of opinion expression. However, due to the multimodal characteristics of meme contents, detecting and analyzing the underlying emotion of a meme is a formidable task. In this paper, we present our approach for detecting the emotion of a meme defined in the SemEval-2020 Task 8. Our team CSECU KDE MA employs an attention-based neural network model to tackle the problem. Upon extracting the text contents from a meme using an optical character reader (OCR), we represent it using the distributed representation of words. Next, we perform the convolution based on multiple kernel sizes to obtain the higher-level feature sequences. The feature sequences are then fed into the attentive time-distributed bidirectional LSTM model to learn the long-term dependencies effectively. Experimental results show that our proposed neural model obtained competitive performance among the participants' systems.

show abstract

Meme Opinion Categorization by Using Optical Character Recognition (OCR) and Naïve Bayes Algorithm

Cited by 16 publications

References 8 publications

Evaluation of deep learning approaches for optical character recognition in Urdu language

Evaluation of deep learning approaches for optical character recognition in Urdu language

Ekstraksi Informasi Meme-Internet Berbahasa Indonesia Dengan Mesin Pencarian

CSECU_KDE_MA at SemEval-2020 Task 8: A Neural Attention Model for Memotion Analysis

Contact Info

Product

Resources

About