scholarly journals YORÙBÁNET: A DEEP CONVOLUTIONAL NEURAL NETWORK DESIGN FOR YORÙBÁ ALPHABETS RECOGNITION

2021 ◽  
Vol 5 (11) ◽  
Author(s):  
Oyeniran Oluwashina Akinloye ◽  
Oyebode Ebenezer Olukunle
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Network Design ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Recognition System ◽  
Test Set ◽  
Novel Technique ◽  
Optical Character

Numerous works have been proposed and implemented in computerization of various human languages, nevertheless, miniscule effort have also been made so as to put Yorùbá Handwritten Character on the map of Optical Character Recognition. This study presents a novel technique in the development of Yorùbá alphabets recognition system through the use of deep learning. The developed model was implemented on Matlab R2018a environment using the developed framework where 10,500 samples of dataset were for training and 2100 samples were used for testing. The training of the developed model was conducted using 30 Epoch, at 164 iteration per epoch while the total iteration is 4920 iterations. Also, the training period was estimated to 11296 minutes 41 seconds. The model yielded the network accuracy of 100% while the accuracy of the test set is 97.97%, with F1 score of 0.9800, Precision of 0.9803 and Recall value of 0.9797.

scholarly journals Optical Character Recognition using CRNN

2020 ◽  
Vol 9 (8) ◽  
pp. 115-120
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Character Recognition ◽  
Web Application ◽  
Optical Character Recognition ◽  
Recognition System ◽  
Feature Maps ◽  
Sequence Modeling ◽  
Optical Character ◽  
Network Component

Optical Character Recognition (OCR) is a computer vision technique which recognizes text present in any form of images, such as scanned documents and photos. In recent years, OCR has improved significantly in the precise recognition of text from images. Though there are many existing applications, we plan on exploring the domain of deep learning and build an optical character recognition system using deep learning architectures. In the later stage, this OCR system is developed to form a web application which provides the functionalities. The approach applied to achieve this is to implement a hybrid model containing three components namely, the Convolutional Neural Network component, the Recurrent Neural Network component and the Transcription component which decodes the output from RNN into the corresponding label sequence. The process of solving problems involving text recognition required CNN to extract feature maps from images. These sequence of feature vectors undergo sequence modeling through the RNN component predicting label distributions which are later translated using the Connectionist Temporal Classification technique in the transcription layer. The model implemented acts as the backend of the web application developed using the Flask web framework. The complete application is later containerized into an image using Docker. This helps in easy deployment on the application along with its environment across any system.

Convolutional Neural Network Based Intelligent Advertisement Search Framework for Online English Newspapers

2021 ◽  
Vol 15 ◽  
Author(s):  
Pooja Jain ◽  
Kavita Taneja ◽  
Harmunish Taneja
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Access To Information ◽  
Intelligent Environment ◽  
Online Newspapers ◽  
Manual Search ◽  
Optical Character ◽  
Extraction Algorithm

Background: Instant access to desired information is crucial for building an intelligent environment that creates value for people and steering towards society 5.0. Online newspapers are one such example that provides instant access to information anywhere and anytime on our mobiles, tablets, laptops, desktops, etc. However, when it comes to searching for a specific advertisement in newspapers, online newspapers do not provide easy advertisement search options. In addition, there are no specialized search portals for keyword-based advertisement searches across multiple online newspapers. As a result, to find a specific advertisement in multiple newspapers, a sequential manual search is required across a range of online newspapers. Objective: This research paper proposes a keyword-based advertisement search framework to provide an instant access to the relevant advertisements from online English newspapers in a category of reader’s choice. Method: First, an image extraction algorithm is proposed to identify and extract the images from online newspapers without using any rules on advertisement placement and size. It is followed by a proposed deep learning Convolutional Neural Network (CNN) model named ‘Adv_Recognizer’ to separate the advertisement images from non-advertisement images. Another CNN Model, ‘Adv_Classifier’, is proposed, classifying the advertisement images into four pre-defined categories. Finally, the Optical Character Recognition (OCR) technique performs keyword-based advertisement searches in various types across multiple newspapers. Results: The proposed image extraction algorithm can easily extract all types of well-bounded images from different online newspapers. This algorithm is used to create an ‘English newspaper image dataset’ of 11,000 images, including advertisements and non-advertisements. The proposed ‘Adv_Recognizer’ model separates advertising and non-advertisement pictures with an accuracy of around 97.8%. In addition, the proposed ‘Adv_Classifier’ model classifies the advertisements in four pre-defined categories exhibiting an accuracy of approximately 73.5%. Conclusion: The proposed framework will help newspaper readers perform exhaustive advertisement searches across various online English newspapers in a category of their interest. It will also help in carrying out advertisement analysis and studies.

scholarly journals Penerapan Convolutional Neural Network (CNN) pada Pengenalan Aksara Lampung Berbasis Optical Character Recognition (OCR)

2021 ◽  
Vol 7 (1) ◽  
pp. 52
Author(s):  
Agus Mulyanto ◽  
Erlina Susanti ◽  
Farli Rossi ◽  
Wajiran Wajiran ◽  
Rohmat Indra Borman
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Optical Character ◽  
Model Training ◽  
Fully Connected

Provinsi Lampung memiliki bahasa dan aksara daerah yang disebut juga dengan Had Lampung atau KaGaNga yang merupakan aksara asli lampung. Melihat bagaimana pentingnya nilai akan eksistensi sebuah budaya dan pentingnya pelestarian aksara lampung maka dibutuhkan teknologi yang membantu dalam mengenalkan aksara lampung, salah satunya dengan teknologi optical character recognition (OCR) yang digunakan untuk merubah citra kedalam teks. Untuk mengenali pola citra Aksara Lampung dan klasifikasi model maka digunakan Convolutional Neural Network (CNN). CNN memiliki lapisan convolution yang terbentuk dari beberapa gabungan lapisan konvolusi, lapisan pooling dan lapisan fully connected. Pada peneilitian yang dilakukan dataset dikembangkan dengan pengumpulan hasil tulis tangan dari sampel responden yang telah ditentukan, kemudian dilakukan scanning gambar. Selanjutnya, dilakukan proses pelabelan dan disimpan dengan format YOLO yaitu TXT. Dari asitektur CNN yang dibangun berdasarkan hasil evaluasi menunjukan loss, accuracy menghasilkan nilai training accuracy mendapatkan nilai sebesar 0.57 dan precision mendapatkan nilai sebesar 0.87. Dari hasil nilai accuracy dan precision menunjukkan bahwa model training sudah baik karena mendekati angka 1.

Sign in / Sign up

Export Citation Format

Share Document