SCENE TEXT RECOGNITION BY USING EE-MSER AND OPTICAL CHARACTER RECOGNITION FOR NATURAL IMAGES

2015 ◽  
Vol 2 (12) ◽  
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Natural Images ◽  
Text Recognition ◽  
Optical Character ◽  
Scene Text ◽  
Scene Text Recognition

Analysis of Text Identification Techniques Using Scene Text and Optical Character Recognition

2021 ◽  
Vol 11 (4) ◽  
pp. 39-62
Author(s):  
Monica Gupta ◽  
Alka Choudhary ◽  
Jyotsna Parmar
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Short Term Memory ◽  
Handwriting Recognition ◽  
Text Recognition ◽  
Stroke Width ◽  
Optical Character ◽  
Scene Text ◽  
Benchmark Datasets ◽  
Scene Text Recognition

In today's era, data in digitalized form is needed for faster processing and performing of all tasks. The best way to digitalize the documents is by extracting the text from them. This work of text extraction can be performed by various text identification tasks such as scene text recognition, optical character recognition, handwriting recognition, and much more. This paper presents, reviews, and analyses recent research expansion in the area of optical character recognition and scene text recognition based on various existing models such as convolutional neural network, long short-term memory, cognitive reading for image processing, maximally stable extreme regions, stroke width transformation, and achieved remarkable results up to 90.34% of F-score with benchmark datasets such as ICDAR 2013, ICDAR 2019, IIIT5k. The researchers have done outstanding work in the text recognition field. Yet, improvement in text detection in low-quality image performance is required, as text identification should not be limited to the input quality of the image.

scholarly journals Aplikasi Kalkulator Tulisan Tangan Sederhana Menggunakan Optical Character Recognition (OCR)

2021 ◽  
Vol 3 (2) ◽  
pp. 103-116
Author(s):  
Supriadi Supriadi
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Text Recognition ◽  
Arithmetic Operations ◽  
Written Text ◽  
Optical Character ◽  
Calculation Results

The calculator is a calculation tool that is widely used in various specialized fields of business and commerce. The use of a calculator makes it easier for humans to perform arithmetic operations, but there are obstacles in the process of inputting numbers if you want to calculate the value of numbers on written media such as paper, whiteboards and so on. The user must first see the text on written media, then read it and remember it then type the writing on a calculator tool or application. The drawback of this method is that when the user forgets the writing on the written media, the user will see the written text and remember it again so that it takes longer to perform calculations using a calculator. The method used in this study is Optical Character Recognition, this method can recognize text contained in images or handwritten images of mathematical number operations. The results of the text recognition will then be carried out by arithmetic calculations to get the calculation results. From the trials on 20 handwritten images of mathematical number operations, the results obtained were 85% accuracy of extraction and accuracy of handwritten images that can be calculated and correct by 85%

scholarly journals Research on Deep Learning Techniques in Breaking Text-Based Captchas and Designing Image-Based Captcha

2021 ◽  
pp. 266-269
Author(s):  
Janarthanan A ◽  
Pandiyarajan C ◽  
Sabarinathan M ◽  
Sudhan M ◽  
Kala R
Keyword(s):  
Deep Learning ◽  
Image Classification ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Experimental Results ◽  
Text Recognition ◽  
Image Resizing ◽  
Optical Character ◽  
Learning Techniques ◽  
Text Images

Optical character recognition (OCR) is a process of text recognition in images (one word). The input images are taken from the dataset. The collected text images are implemented to pre-processing. In pre-processing, we can implement the image resize process. Image resizing is necessary when you need to increase or decrease the total number of pixels, whereas remapping can occur when you are zooming refers to increase the quantity of pixels, so that when you zoom an image, you will see clear content. After that, we can implement the segmentation process. In segmentation, we can segment the each characters in one word. We can extract the features values from the image that means test feature. In classification process, we have to classify the text from the image. Image classification is performed the images in order to identify which image contains text. A classifier is used to identify the image containing text. The experimental results shows that the accuracy.

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 Improving Optical Character Recognition Techniques

2018 ◽  
Vol 7 (2.24) ◽  
pp. 361 ◽  
Author(s):  
Nitin Ramesh ◽  
Aksha Srivastava ◽  
K Deeba
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Document Image ◽  
Text Recognition ◽  
Digital Form ◽  
Written Text ◽  
Optical Character ◽  
Research Organizations ◽  
The World

Document text recognition uses a concept called OCR (optical character recognition),which is the recognition of printed or written text characters by a computer. This involves scanning a document containing text, and converting character by character to their digital form. Thus, it is defined as the process of digitizing a document image into its constituent characters. Equipment used to obtain clearer images for analysis are cameras and flatbed scanners. Even though it’s been out in the world since 1870, the OCR technology is yet to reach perfection. This demanding nature of Optical Character Recognition has made various researchers, industries and technology enthusiasts to divulge their attention to this field. In recent times one can notice a significant increase in the number of research organizations investing their time and effort in this field. In this research, the progress, different aspects and various issues revolving in this field have been summarized. The aim is to present a scrupulous overview of various proposals, advancements and discussions aimed at resolving various problems that arise in traditional OCR.  

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.

scholarly journals Learning to Draw Text in Natural Images with Conditional Adversarial Networks

2019 ◽  
Author(s):  
Shancheng Fang ◽  
Hongtao Xie ◽  
Jianjun Chen ◽  
Jianlong Tan ◽  
Yongdong Zhang
Keyword(s):  
Image Synthesis ◽  
Recognition Algorithm ◽  
Natural Images ◽  
Text Recognition ◽  
Local Character ◽  
Adversarial Networks ◽  
Scene Text ◽  
Style Consistency ◽  
Text Images ◽  
Scene Text Recognition

In this work, we propose an entirely learning-based method to automatically synthesize text sequence in natural images leveraging conditional adversarial networks. As vanilla GANs are clumsy to capture structural text patterns, directly employing GANs for text image synthesis typically results in illegible images. Therefore, we design a two-stage architecture to generate repeated characters in images. Firstly, a character generator attempts to synthesize local character appearance independently, so that the legible characters in sequence can be obtained. To achieve style consistency of characters, we propose a novel style loss based on variance-minimization. Secondly, we design a pixel-manipulation word generator constrained by self-regularization, which learns to convert local characters to plausible word image. Experiments on SVHN dataset and ICDAR, IIIT5K datasets demonstrate our method is able to synthesize visually appealing text images. Besides, we also show the high-quality images synthesized by our method can be used to boost the performance of a scene text recognition algorithm.

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