scholarly journals Optical Character Recognition based Webapp

2021 ◽  
Vol 9 (VI) ◽  
pp. 385-389
Author(s):  
Akshay Gharde
Keyword(s):  
Character Recognition ◽  
Web Application ◽  
Optical Character Recognition ◽  
Digital Conversion ◽  
Daily Lives ◽  
Storage And Retrieval ◽  
Optical Character ◽  
Textual Data ◽  
Cross Platform ◽  
Natural Way

As the use of computers in our daily lives increases, so has the need for a natural procedure to interact with the computers. The ultimate aim of human computer interaction is to bring the change that there is always a natural way of interacting with computers coupled with ease and flexibility. Printed and textual media such as prescriptions, invoices, receipts, etc. occupies a large segment of our day-to-day activities and given their volume, it is inefficient to manage them physically as there’s always an associated risk of fading, damage, misplacing, etc. and hence a medium is required for their digital conversion. In this project, we have developed a robust, cross-platform web application that can process the images using PyTesseract based algorithms that can efficiently extract the textual data to facilitate the storage and retrieval of the same. The extracted text can be downloaded as a text file and can also be translated into the desired language. This is an active field of research and thus this paper also discusses various current implementations of the mentioned concept. The Optical Character Recognition framework finds applications in a variety of fields such as business process activities, number plate recognition, KYC and banking processes to name a few.

scholarly journals OPTICAL CHARACTER RECOGNITION FOR ELECTRONIC INVOICES USING AWS SERVICES

2021 ◽  
Vol 6 (5) ◽  
Author(s):  
Sameer M. Patel ◽  
Sarvesh S. Pai ◽  
Mittal B. Jain ◽  
Vaibhav P. Vasani
Keyword(s):  
Character Recognition ◽  
Web Application ◽  
Optical Character Recognition ◽  
Credit Cards ◽  
Text Recognition ◽  
Service Architecture ◽  
The Past ◽  
Optical Character ◽  
Handwritten Text

Optical Character Recognition is basically the mechanical or electronic conversion of printed or handwritten text into machine understandable text. The complication of Optical Character Recognition in different conditions remains as relevant as it was in the past few years. At the present time of automation and innovations, Keyboarding remains the most common way of inputting or feeding data into computers. This is probably the most time consuming and labor-intensive operation in the industry. Automating the process of recognition of documents, credit cards, electronic invoices, and license plates of cars – all of this could help in saving time for analyzing and processing data. With the increased research and development of machine learning, the quality of text recognition is continuously growing better. Our paper is focused on providing a brief explanation of the different stages involved in the process of optical character recognition and through the proposed application; we aim to automate the process of extraction of important texts from electronic invoices. The main goal of the project is to develop a real time OCR web application with a micro service architecture, which would help in extracting necessary information from an invoice.

Finger Spelling in Air System for Deaf and Dumb

2018 ◽  
Vol 9 (1) ◽  
pp. 28-44
Author(s):  
Urmila Shrawankar ◽  
Shruti Gedam
Keyword(s):  
Real Time ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Human Interaction ◽  
Finger Movements ◽  
Accuracy Rate ◽  
Optical Character ◽  
Average Accuracy ◽  
Bright Color ◽  
Natural Way

Finger spelling in air helps user to operate a computer in order to make human interaction easier and faster than keyboard and touch screen. This article presents a real-time video based system which recognizes the English alphabets and words written in air using finger movements only. Optical Character Recognition (OCR) is used for recognition which is trained using more than 500 various shapes and styles of all alphabets. This system works with different light situations and adapts automatically to various changing conditions; and gives a natural way of communicating where no extra hardware is used other than system camera and a bright color tape. Also, this system does not restrict writing speed and color of tape. Overall, this system achieves an average accuracy rate of character recognition for all alphabets of 94.074%. It is concluded that this system is very useful for communication with deaf and dumb people.

KANNADA ALPHABETS RECOGNITION WITH APPLICATION TO BRAILLE TRANSLATION

2011 ◽  
Vol 11 (03) ◽  
pp. 293-314
Author(s):  
SIDDHALING UROLAGIN ◽  
K. V. PREMA ◽  
N. V. SUBBA REDDY
Keyword(s):  
Decision Tree ◽  
Decision Trees ◽  
Prior Knowledge ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Research Work ◽  
Indian Languages ◽  
Indian Language ◽  
Optical Character ◽  
Natural Way

In this paper, an effort is made to apply optical character recognition (OCR) for Braille translation on Kannada characters. In general, OCR systems for Indian language are more complex due to larger number of vowels, consonants, and conjuncts and Indian languages are inflectional and agglutinative in nature. Specifically, characters of Kannada script have higher similarity in shape and higher variability across fonts, making recognition of characters a difficult task. A decision tree is developed in this research work. The main motivations are that decision trees provide a natural way to incorporate prior knowledge of domain and many Kannada characters have similar looking shapes. The similar looking characters can be grouped and then further partitioned into categories at various levels to effectively create a decision tree. To facilitate this, three modular classifiers are developed based on the nature of Kannada characters. These modular classifiers are employed at nodes of the decision tree. The Braille equivalent of Kannada characters is obtained by translation rules. An overall accuracy of classification and Braille translation of 93.80% is obtained.

Data Extraction from Images through OCR

2021 ◽  
Vol 9 (VIII) ◽  
pp. 435-437
Author(s):  
Anurag Tiwari
Keyword(s):  
Small Businesses ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Human Error ◽  
Data Extraction ◽  
Data Accessibility ◽  
Daily Lives ◽  
Whole Process ◽  
Optical Character ◽  
User Data

The paperwork used in maintaining various types of documents in our daily lives is tiresome and inefficient, it consumes a lot of time and it is difficult to maintain and remember the concerned documents. This project provides a solution to these problems by introducing Optical Character Recognition Technology (OCR) which runs on Tesseract OCR Engine. The project specifically aims at increasing data accessibility, usability and improving customer experience by decreasing the time spent to process, save, and maintain user data. Another objective of this project is to nullify the human error, which is huge in manual handling of data records, the software used in the solution uses certain techniques to minimize these errors. Optical Character Recognition (OCR) is used for extracting texts and characters from an image. This helps us in maintaining our records and data digitally and securely. In this project we are using the Tesseract OCR Engine which has high accuracy rates for clean images. We have implemented a web version of OCR which runs on TesseractJS; other JavaScript frameworks are also used. The outcome of the project is that it is able successfully to extract text and characters from the provided image using Tesseract OCR Engine. It is observed that for the high resolution images the accuracy is above 90%. This web based application is useful for small businesses as they don’t have to install any extra software, all it needs is a file to be uploaded on an online interface making them able to access remotely. It will also help students to save notes and documents online which will make their important documents easily accessible on the web. This whole process is time and memory efficient.

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.

A Structured Method for the Recognition of Complex Historical Tables

1997 ◽  
Vol 9 (1-3) ◽  
pp. 58-77
Author(s):  
Vitaly Kliatskine ◽  
Eugene Shchepin ◽  
Gunnar Thorvaldsen ◽  
Konstantin Zingerman ◽  
Valery Lazarev
Keyword(s):  
Nineteenth Century ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Complex Structure ◽  
Source Material ◽  
Historical Sources ◽  
Tax Assessment ◽  
Optical Character ◽  
Algorithmic Model ◽  
Machine Readable

In principle, printed source material should be made machine-readable with systems for Optical Character Recognition, rather than being typed once more. Offthe-shelf commercial OCR programs tend, however, to be inadequate for lists with a complex layout. The tax assessment lists that assess most nineteenth century farms in Norway, constitute one example among a series of valuable sources which can only be interpreted successfully with specially designed OCR software. This paper considers the problems involved in the recognition of material with a complex table structure, outlining a new algorithmic model based on ‘linked hierarchies’. Within the scope of this model, a variety of tables and layouts can be described and recognized. The ‘linked hierarchies’ model has been implemented in the ‘CRIPT’ OCR software system, which successfully reads tables with a complex structure from several different historical sources.

CNN-based Rain Reduction in Street View Images

2020 ◽  
Vol 2020 (1) ◽  
pp. 78-81
Author(s):  
Simone Zini ◽  
Simone Bianco ◽  
Raimondo Schettini
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
State Of The Art ◽  
Weather Conditions ◽  
Specific Interest ◽  
Optical Character ◽  
Street View ◽  
In The Wild ◽  
Bad Weather ◽  
Detection And Recognition

Rain removal from pictures taken under bad weather conditions is a challenging task that aims to improve the overall quality and visibility of a scene. The enhanced images usually constitute the input for subsequent Computer Vision tasks such as detection and classification. In this paper, we present a Convolutional Neural Network, based on the Pix2Pix model, for rain streaks removal from images, with specific interest in evaluating the results of the processing operation with respect to the Optical Character Recognition (OCR) task. In particular, we present a way to generate a rainy version of the Street View Text Dataset (R-SVTD) for "text detection and recognition" evaluation in bad weather conditions. Experimental results on this dataset show that our model is able to outperform the state of the art in terms of two commonly used image quality metrics, and that it is capable to improve the performances of an OCR model to detect and recognise text in the wild.

Sign in / Sign up

Export Citation Format

Share Document