Hindi Optical Character Recognition and Its Applications

2017 ◽  
pp. 28-39
Author(s):  
Rashmi Gupta ◽  
Dipti Gupta ◽  
Megha Dua ◽  
Manju Khari
Keyword(s):  
Computer Vision ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Handwritten Documents ◽  
Library Automation ◽  
Optical Character ◽  
Machine Printed Documents ◽  
Processing Techniques ◽  
Handwritten Recognition ◽  
Hand Writing

Recognition is an important part in the computer vision. Optical character recognition is nowadays gaining its importance in terms of the digital and handwritten documents recognition. Devanagari is widely spoken script with more than 300 million people relying on it for their day-to-day activities, so recognition of Devanagari characters is gaining its importance in the recent times. Tasksin handwritten recognition handle the differences along with alteration of Hindi characters written in offline mode. Furthermore, Hindi character are written in different sizes shapes and orientation in contrast to hand writing usually written along a particular baseline in a horizontal direction. Handwritten and machine printed documents are needed to be recognized for the applications like bank Cheque processing, library automation, publication house, manuscripts, Granths and other forms and documents. In this paper an attempt has been made to shortlist the methods and processing techniques studied so far in the field of Devanagari character recognition. The performance analysis and the results for the various techniques are given in the chapter.

scholarly journals Handwritten Bangla Numerical Digit Recognition Using Fine Regulated Deep Neural Network

2021 ◽  
Vol 9 (2) ◽  
pp. 73-84
Author(s):  
Md. Shahadat Hossain ◽  
Md. Anwar Hossain ◽  
AFM Zainul Abadin ◽  
Md. Manik Ahmed
Keyword(s):  
Neural Network ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Network Performance ◽  
Deep Neural Network ◽  
Recognition Accuracy ◽  
Digit Recognition ◽  
Optical Character ◽  
Regularization Parameters ◽  
Handwritten Recognition

The recognition of handwritten Bangla digit is providing significant progress on optical character recognition (OCR). It is a very critical task due to the similar pattern and alignment of handwriting digits. With the progress of modern research on optical character recognition, it is reducing the complexity of the classification task by several methods, a few problems encounter during recognition and wait to be solved with simpler methods. The modern emerging field of artificial intelligence is the Deep Neural Network, which promises a solid solution to these few handwritten recognition problems. This paper proposed a fine regulated deep neural network (FRDNN) for the handwritten numeric character recognition problem that uses convolutional neural network (CNN) models with regularization parameters which makes the model generalized by preventing the overfitting. This paper applied Traditional Deep Neural Network (TDNN) and Fine regulated deep neural network (FRDNN) models with a similar layer experienced on BanglaLekha-Isolated databases and the classification accuracies for the two models were 96.25% and 96.99%, respectively over 100 epochs. The network performance of the FRDNN model on the BanglaLekha-Isolated digit dataset was more robust and accurate than the TDNN model and depend on experimentation. Our proposed method is obtained a good recognition accuracy compared with other existing available methods.

scholarly journals Recognition of Handwritten Characters using Deep Convolutional Neural Network

2019 ◽  
Vol 8 (6S4) ◽  
pp. 314-317
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Recognition Rate ◽  
Writing Style ◽  
Handwritten Character Recognition ◽  
New Approach ◽  
Optical Character ◽  
Handwritten Character ◽  
Handwritten Recognition ◽  
Difficult Part

Handwritten character recognition (HCR) mainly entails optical character recognition. However, HCR involves in formatting and segmentation of the input. HCR is still an active area of research due to the fact that numerous verification in writing style, shape, size to individuals. The main difficult part of Indian handwritten recognition has overlapping between characters. These overlapping shaped characters are difficult to recognize that may lead to low recognition rate. These factors also increase the complexity of handwritten character recognition. This paper proposes a new approach to identify handwritten characters for Telugu language using Deep Learning (DL). The proposed work can be enhance the recognition rate of individual characters. The proposed approach recognizes with overall accuracy is 94%.

scholarly journals Computer Vision for Continuous Bedside Pharmacological Data Extraction: A Novel Application of Artificial Intelligence for Clinical Data Recording and Biomedical Research

2021 ◽  
Vol 4 ◽  
Author(s):  
Logan Froese ◽  
Joshua Dian ◽  
Carleen Batson ◽  
Alwyn Gomez ◽  
Amanjyot Singh Sainbhi ◽  
...  
Keyword(s):  
Computer Vision ◽  
Data Integration ◽  
Real Time ◽  
Clinical Data ◽  
Biomedical Research ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Time Data ◽  
Optical Character ◽  
Real Time Data

Introduction: As real time data processing is integrated with medical care for traumatic brain injury (TBI) patients, there is a requirement for devices to have digital output. However, there are still many devices that fail to have the required hardware to export real time data into an acceptable digital format or in a continuously updating manner. This is particularly the case for many intravenous pumps and older technological systems. Such accurate and digital real time data integration within TBI care and other fields is critical as we move towards digitizing healthcare information and integrating clinical data streams to improve bedside care. We propose to address this gap in technology by building a system that employs Optical Character Recognition through computer vision, using real time images from a pump monitor to extract the desired real time information.Methods: Using freely available software and readily available technology, we built a script that extracts real time images from a medication pump and then processes them using Optical Character Recognition to create digital text from the image. This text was then transferred to an ICM + real-time monitoring software in parallel with other retrieved physiological data.Results: The prototype that was built works effectively for our device, with source code openly available to interested end-users. However, future work is required for a more universal application of such a system.Conclusion: Advances here can improve medical information collection in the clinical environment, eliminating human error with bedside charting, and aid in data integration for biomedical research where many complex data sets can be seamlessly integrated digitally. Our design demonstrates a simple adaptation of current technology to help with this integration.

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.

scholarly journals Embedded system design to control the entry and exit of vehicles online, at the main access of ESPOCH

2018 ◽  
Vol 3 (CITT2017) ◽  
pp. 113-120
Author(s):  
Javier J. Gavilanes ◽  
Jairo R. Jácome ◽  
Alexandra O. Pazmiño
Keyword(s):  
Embedded System ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Entry And Exit ◽  
Real Time System ◽  
Optical Character ◽  
Nearest Neighbours ◽  
Processing Techniques ◽  
Python Programming ◽  
Main Entrance

In this research a embedded real-time system was developed by using Raspberry Pi3 (a reduced board computer), which is an equipment with a camera placed in strategic points of the mechanic arms at the main entrance and exit of Escuela Superior Politécnica de Chimborazo, this equipment captures images of vehicles that enter and exit the campus and the information is extracted through the implementation of a segmentation algorithm written in Python programming language and the collaboration of artificial vision bookstores offered by OpenCV, processing techniques were applied to extract the vehicle plate from the location scenery. Then, an Optical Character Recognition (OCR) algorithm also known as K-Nearest Neighbours (KNN) was applied, which after a training phase is able to identify letters and numbers on the automobile plates, the information is stored in the entrance database and it is deleted when the automobile exits the campus.

scholarly journals IMPLEMENTASI PERMAINAN FLOW PADA PEMBANGUNAN SISTEM CAPTCHA

2016 ◽  
Vol 11 (2) ◽  
Author(s):  
Indra Setiawan ◽  
Willy Sudiarto Raharjo ◽  
Budi Susanto
Keyword(s):  
Computer Vision ◽  
Success Rate ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Completion Time ◽  
High Probability ◽  
Web Applications ◽  
Optical Character ◽  
Flow Game ◽  
New System

The basic challenge in designing an obfuscating CAPTCHAs is to make them easy enough that users are not dissuaded from attempting a solution, yet still too difficult to solve using available image-based computer vision algorithms. CAPTCHA has been widely used in many web applications and there has been so many research on CAPTCHA. Current technology enables computer to easily solve image-based CAPTCHA with high probability, so we propose another type of CAPTCHA-based authenticaton that can not be solved by utilizing Optical Character Recognition but still easy to use for new users. We implemented the new model of CAPTCHA using FLOW game. We found that the success rate of this new system is 92.025%, completion time is 6.3614s, and 81,67% of users are able to solve it in less than 10s.

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.

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