scholarly journals Characters recognition using keys points and convolutional neural network

2021 ◽  
Vol 22 (3) ◽  
pp. 1629
Author(s):  
M. Boutounte ◽  
Y. Ouadid
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Basic Method ◽  
Royal Institute ◽  
Original Image ◽  
Optical Character ◽  
Harris Corner

<p>In this paper, the convolutional neural network (CNN) is used in order to design an efficient optical character recognition (OCR) system for the Tifinagh characters. indeed, this approach has proved a greater efficiency by giving an accuracy of 99%, this approach based in keys points detection using Harris corner method, the detected points are automatically added to the original image to create a new database compared to the basic method that use directly the database after a preprocessing step consisting on normalization and thinning the characters. Using this method, we can benefit from the power of the convolutional neural network as classifier in image that has already the feature. The test was performed on the Moroccan Royal Institute of Amazigh Culture (IRCAM) database composed of 33000 characters of different size and style what present the difficulty, the keys points are the same in the printed and handwritten characters so this method can be apply in both type with some modifications.</p>

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 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 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.

scholarly journals Automatic Annotation of Events and Highlights Generation of Cricket Match Videos

2019 ◽  
Vol 8 (11) ◽  
pp. 3878-3881
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Full Length ◽  
Automatic Annotation ◽  
Time Intervals ◽  
Optical Character ◽  
Novel Method

In this paper, we propose a novel method for automatic annotation of events and highlights generation for cricket match videos. The videos are divided into time intervals representing one ball clip using a Convolutional Neural Network (CNN) and Optical Character Recognition (OCR). CNN detects the start frame of a ball. OCR is used to detect the end of the ball and to annotate it by recognizing the change in runs/wickets. The proposed framework is able to annotate events and generate sufficiently good highlights for four full length cricket matches.

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