Modified Convolutional Neural Network of Tamil Character Recognition

2020 ◽  
pp. 469-480 ◽  
Author(s):  
C. Vinotheni ◽  
S. Lakshmana Pandian ◽  
G. Lakshmi
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Character Recognition

scholarly journals BengaliNet: A Low-Cost Novel Convolutional Neural Network for Bengali Handwritten Characters Recognition

2021 ◽  
Vol 11 (15) ◽  
pp. 6845
Author(s):  
Abu Sayeed ◽  
Jungpil Shin ◽  
Md. Al Mehedi Hasan ◽  
Azmain Yakin Srizon ◽  
Md. Mehedi Hasan
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Character Recognition ◽  
High Performance ◽  
Low Cost ◽  
Traditional Learning ◽  
Neural Network Architecture ◽  
Handwritten Character Recognition ◽  
Handwritten Character

As it is the seventh most-spoken language and fifth most-spoken native language in the world, the domain of Bengali handwritten character recognition has fascinated researchers for decades. Although other popular languages i.e., English, Chinese, Hindi, Spanish, etc. have received many contributions in the area of handwritten character recognition, Bengali has not received many noteworthy contributions in this domain because of the complex curvatures and similar writing fashions of Bengali characters. Previously, studies were conducted by using different approaches based on traditional learning, and deep learning. In this research, we proposed a low-cost novel convolutional neural network architecture for the recognition of Bengali characters with only 2.24 to 2.43 million parameters based on the number of output classes. We considered 8 different formations of CMATERdb datasets based on previous studies for the training phase. With experimental analysis, we showed that our proposed system outperformed previous works by a noteworthy margin for all 8 datasets. Moreover, we tested our trained models on other available Bengali characters datasets such as Ekush, BanglaLekha, and NumtaDB datasets. Our proposed architecture achieved 96–99% overall accuracies for these datasets as well. We believe our contributions will be beneficial for developing an automated high-performance recognition tool for Bengali handwritten characters.

scholarly journals Automated hierarchical classification of scanned documents using convolutional neural network and regular expression

2022 ◽  
Vol 12 (1) ◽  
pp. 1018
Author(s):  
Rifiana Arief ◽  
Achmad Benny Mutiara ◽  
Tubagus Maulana Kusuma ◽  
Hustinawaty Hustinawaty
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Regular Expression ◽  
Hierarchical Classification ◽  
Document Vector ◽  
Classification Prediction ◽  
Scanned Documents

<p>This research proposed automated hierarchical classification of scanned documents with characteristics content that have unstructured text and special patterns (specific and short strings) using convolutional neural network (CNN) and regular expression method (REM). The research data using digital correspondence documents with format PDF images from pusat data teknologi dan informasi (technology and information data center). The document hierarchy covers type of letter, type of manuscript letter, origin of letter and subject of letter. The research method consists of preprocessing, classification, and storage to database. Preprocessing covers extraction using Tesseract optical character recognition (OCR) and formation of word document vector with Word2Vec. Hierarchical classification uses CNN to classify 5 types of letters and regular expression to classify 4 types of manuscript letter, 15 origins of letter and 25 subjects of letter. The classified documents are stored in the Hive database in Hadoop big data architecture. The amount of data used is 5200 documents, consisting of 4000 for training, 1000 for testing and 200 for classification prediction documents. The trial result of 200 new documents is 188 documents correctly classified and 12 documents incorrectly classified. The accuracy of automated hierarchical classification is 94%. Next, the search of classified scanned documents based on content can be developed.</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.

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