Literature Survey on Student Grade Calculation using Optical Character Recognition based Image Processing Techniques

2021 ◽  
Vol 7 (1) ◽  
pp. 34-41
Author(s):  
Omkiran S G ◽  
Samartha J V ◽  
Shashank Nagraj Bhat ◽  
Varun Gajanan Hegde ◽  
Sumaiya M N
Keyword(s):  
Image Processing ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Literature Survey ◽  
Image Processing Techniques ◽  
Optical Character ◽  
Processing Techniques

Efficient Image Denoising for Effective Digitization using Image Processing Techniques and Neural Networks

2016 ◽  
Vol 7 (4) ◽  
pp. 77-93 ◽  
Author(s):  
K.G. Srinivasa ◽  
B.J. Sowmya ◽  
D. Pradeep Kumar ◽  
Chetan Shetty
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Network Models ◽  
Morphological Operations ◽  
Neural Network Models ◽  
Image Processing Techniques ◽  
Optical Character ◽  
Processing Techniques

Vast reserves of information are found in ancient texts, scripts, stone tablets etc. However due to difficulty in creating new physical copies of such texts, knowledge to be obtained from them is limited to those few who have access to such resources. With the advent of Optical Character Recognition (OCR) efforts have been made to digitize such information. This increases their availability by making it easier to share, search and edit. Many documents are held back due to being damaged. This gives rise to an interesting problem of removing the noise from such documents so it becomes easier to apply OCR on them. Here the authors aim to develop a model that helps denoise images of such documents retaining on the text. The primary goal of their project is to help ease document digitization. They intend to study the effects of combining image processing techniques and neural networks. Image processing techniques like thresholding, filtering, edge detection, morphological operations, etc. will be applied to pre-process images to yield higher accuracy of neural network models.

Use of Deep Neural Network for Optical Character Recognition

2020 ◽  
pp. 219-254
Author(s):  
Abhishek Das ◽  
Mihir Narayan Mohanty
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Comparative Study ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Deep Neural Network ◽  
Detailed Review ◽  
Image Processing Techniques ◽  
Optical Character ◽  
Processing Techniques

In this chapter, the authors have given a detailed review on optical character recognition. Various methods are used in this field with different accuracy levels. Still there are some difficulties in recognizing handwritten characters because of different writing styles of different individuals even in a particular language. A comparative study is given to understand different types of optical character recognition along with different methods used in each type. Implementation of neural network in different forms is found in most of the works. Different image processing techniques like OCR with CNN, RNN, combination of CNN and RNN, etc. are observed in recent research works.

scholarly journals Automated detection of vehicles license plate using image processing techniques

2020 ◽  
Vol 18 (3) ◽  
pp. 1408
Author(s):  
Farhana Ahmad Poad ◽  
Noor Shuraya Othman ◽  
Roshayati Yahya Atan ◽  
Jusrorizal Fadly Jusoh ◽  
Mumtaz Anwar Hussin
Keyword(s):  
Image Processing ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Region Of Interest ◽  
Automated Detection ◽  
License Plate ◽  
Image Processing Techniques ◽  
Individual Character ◽  
Split And Merge ◽  
Processing Techniques

The aim of this project is to design an Automated Detection of License Plate (ADLP) system based on image processing techniques. There are two techniques that are commonly used in detecting the target, which are the Optical Character Recognition (OCR) and the split and merge segmentation. Basically, the OCR technique performs the operation using individual character of the license plate with alphanumeri characteristic. While, the split and merge segmentation technique split the image of captured plate into a region of interest. These two techniques are utilized and implemented using MATLAB software and the performance of detection is tested on the image and a comparison is done between both techniques. The results show that both techniques can perform well for license plate with some error.

Efficient Image Denoising for Effective Digitization Using Image Processing Techniques and Neural Networks

2018 ◽  
pp. 1091-1108
Author(s):  
K.G. Srinivasa ◽  
B.J. Sowmya ◽  
D. Pradeep Kumar ◽  
Chetan Shetty
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Network Models ◽  
Morphological Operations ◽  
Interesting Problem ◽  
Neural Network Models ◽  
Image Processing Techniques ◽  
Processing Techniques

Vast reserves of information are found in ancient texts, scripts, stone tablets etc. However due to difficulty in creating new physical copies of such texts, knowledge to be obtained from them is limited to those few who have access to such resources. With the advent of Optical Character Recognition (OCR) efforts have been made to digitize such information. This increases their availability by making it easier to share, search and edit. Many documents are held back due to being damaged. This gives rise to an interesting problem of removing the noise from such documents so it becomes easier to apply OCR on them. Here the authors aim to develop a model that helps denoise images of such documents retaining on the text. The primary goal of their project is to help ease document digitization. They intend to study the effects of combining image processing techniques and neural networks. Image processing techniques like thresholding, filtering, edge detection, morphological operations, etc. will be applied to pre-process images to yield higher accuracy of neural network models.

The Application of Image Recognition Based on BP Neural Networks in Automatic Steel Rolling

2013 ◽  
Vol 764 ◽  
pp. 161-164
Author(s):  
Wei Jiang
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Image Recognition ◽  
Character Recognition ◽  
Video Image ◽  
Bp Neural Networks ◽  
Steel Rolling ◽  
Image Processing Techniques ◽  
Processing Techniques ◽  
Frame Grabber

A BP neural networks is presented for billet character recognition. Firstly, by a series of image processing techniques, the character’feature in the billet character region of the video image gathered by frame grabber is abstracted. Secondly, the BP neural networks algorithm is employed for character recognition. Application results show that the image recognition based BP neural networks can performs well in billet character recognition, and the method presented is speedy, efficient and of high value in practice.

Arabic Optical Character Recognition

2011 ◽  
pp. 324-346 ◽  
Author(s):  
Husni Al-Muhtaseb ◽  
Rami Qahwaji
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Arabic Language ◽  
Text Recognition ◽  
Text Segmentation ◽  
Future Trends ◽  
Optical Character ◽  
Arabic Ocr ◽  
Processing Techniques ◽  
Arabic Speaking

Arabic text recognition is receiving more attentions from both Arabic and non-Arabic-speaking researchers. This chapter provides a general overview of the state-of-the-art in Arabic Optical Character Recognition (OCR) and the associated text recognition technology. It also investigates the characteristics of the Arabic language with respect to OCR and discusses related research on the different phases of text recognition including: pre-processing and text segmentation, common feature extraction techniques, classification methods and post-processing techniques. Moreover, the chapter discusses the available databases for Arabic OCR research and lists the available commercial Software. Finally, it explores the challenges related to Arabic OCR and discusses possible future trends.

An Useful Review on Optical Character Recognition for Smart Era Generation

2021 ◽  
pp. 1-41
Author(s):  
Abhishek Das ◽  
Mihir Narayan Mohanty
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Image Processing ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Deep Neural Network ◽  
Machine Learning Method ◽  
Learning Method ◽  
Result Section ◽  
Optical Character

In this chapter, the authors have reviewed on optical character recognition. The study belongs to both typed characters and handwritten character recognition. Online and offline character recognition are two modes of data acquisition in the field of OCR and are also studied. As deep learning is the emerging machine learning method in the field of image processing, the authors have described the method and its application of earlier works. From the study of the recurrent neural network (RNN), a special class of deep neural network is proposed for the recognition purpose. Further, convolutional neural network (CNN) is combined with RNN to check its performance. For this piece of work, Odia numerals and characters are taken as input and well recognized. The efficacy of the proposed method is explained in the result section.

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