scholarly journals MAIL SORTER USING LABVIEW

2012 ◽  
pp. 123-125
Author(s):  
A. NIKHILA ◽  
NISHA A NAIR ◽  
KUMUDA S ◽  
PREETHI K MANE
Keyword(s):  
Code Generation ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
The State ◽  
Labview Software ◽  
The World ◽  
Sorting Process ◽  
Human Effort ◽  
Labview Program ◽  
Real Time Application

The Indian postal system is the largest networks in the world. Being the 7th largest country in the world, major population of the country is rural based, where the basic amenities of life is a sweet dream. In such a scenario, having an efficient mail delivery system is essential. Hence, to eliminate the drawbacks in other processes, we propose to fully automate the sorting process. Unlike the code generation technique, it neither requires any human intervention to generate a code based on the pin code nor will be a problem in case of absence of the pin code. The principle used for sorting is the Optical Character Recognition using LabVIEW software. Camera, placed over the slide unit captures the image of the address. The pin code or the state (in the absence of the pin code) is selected and compared with a set of trained characters in the data base. On finding a positive match, based on the first two digits of the pin code or the first four letters of the state, the mail is segregated by the LabVIEW program involving OCR technique. The processed data is sent to the real time application by the DAQ card, which activates the actuating arm(servo motor) to allow the letters to move to the respective stack(zone) and thus sorting the mails automatically, reducing the human effort and errors.

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 IMPLEMENTASI ALGORITMA YOLO DAN TESSERACT OCR PADA SISTEM DETEKSI PLAT NOMOR OTOMATIS

2022 ◽  
Vol 16 (1) ◽  
pp. 54
Author(s):  
Imam Husni Al amin ◽  
Awan Aprilino
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Detection System ◽  
Extraction Process ◽  
Test Results ◽  
Web Based ◽  
Detection Systems ◽  
Human Labor ◽  
Cross Platform ◽  
Human Effort

Currently, vehicle number plate detection systems in general still use the manual method. This will take a lot of time and human effort. Thus, an automatic vehicle number plate detection system is needed because the number of vehicles that continues to increase will burden human labor. In addition, the methods used for vehicle number plate detection still have low accuracy because they depend on the characteristics of the object being used. This study develops a YOLO-based automatic vehicle number plate detection system. The dataset used is a pretrained YOLOv3 model of 700 data. Then proceed with the number plate text extraction process using the Tesseract Optical Character Recognition (OCR) library and the results obtained will be stored in the database. This system is web-based and API so that it can be used online and on the cross-platform. The test results show that the automatic number plate detection system reaches 100% accuracy with sufficient lighting and a threshold of 0.5 and for the results using the Tesseract library, the detection results are 92.32% where the system is successful in recognizing all characters on the license plates of cars and motorcycles. in the form of Alphanumeric characters of 7-8 characters.

Possible Approaches for Character Recognition With Existing Methodologies and State-of-the-Art Techniques

2019 ◽  
pp. 232-246
Author(s):  
Rashmi Welekar ◽  
Nileshsingh V. Thakur
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
State Of The Art ◽  
Industrial Applications ◽  
New Methods ◽  
Optical Character ◽  
The World ◽  
New Hypothesis ◽  
Key Questions ◽  
Art Techniques

The world started to talk about optical character recognition (OCR) around 1870. Then over another 25 years OCR systems were designed for industrial applications. And now the OCR software is easily available online for free, through products like Acrobat reader, WebOCR, etc. But still the research is on. Do we need to switch direction or introduce new hypothesis are some of the key questions? The purpose of this chapter is to answer the above questions and propose new methods for character recognition.

OPTICAL CHARACTER RECOGNITION — A SURVEY

1991 ◽  
Vol 05 (01n02) ◽  
pp. 1-24 ◽  
Author(s):  
S. IMPEDOVO ◽  
L. OTTAVIANO ◽  
S. OCCHINEGRO
Keyword(s):  
Feature Extraction ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
State Of The Art ◽  
The State ◽  
Historical Background ◽  
Optical Scanner ◽  
Optical Character ◽  
Recognition Systems

In order to highlight the interesting problems and actual results on the state of the art in optical character recognition (OCR), this paper describes and compares preprocessing, feature extraction and postprocessing techniques for commercial reading machines. Problems related to handwritten and printed character recognition are pointed out, and the functions and operations of the major components of an OCR system are described. Historical background on the development of character recognition is briefly given and the working of an optical scanner is explained. The specifications of several recognition systems that are commercially available are reported and compared.

scholarly journals Utilizing Optical Character Recognition and Boarder Detection Algorithms to Identify Trading Cards

10.29007/qkhd ◽  
2019 ◽  
Author(s):  
Brodie Boldt ◽  
Christopher Cooper ◽  
Ryan Fox ◽  
Jared Parks ◽  
Erin Keith
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Card Game ◽  
Detection Algorithms ◽  
Digital Database ◽  
Optical Character ◽  
The World ◽  
Recognition Software ◽  
Protective Case

Magic: The Gathering is a popular physical trading card game played by millions of people around the world. To keep track of their cards, players typically store them in some sort of physical protective case, which can become cumbersome to sort through as the number of cards can reach up to the thousands. By utilizing and improving optical character recognition software, the TCG Digitizer allows users to efficiently store their entire inventory of Magic: The Gathering trading cards in a digital database. With an emphasis on quick and accurate scanning, the final product provides an intuitive digital solution for storing Magic: The Gathering cards for both collectors and card owners who want to easily store their collection of cards on a computer.

scholarly journals Introduction to the Symposium: Improving access to hidden scientific data in the Biodiversity Heritage Library

2019 ◽  
Vol 3 ◽  
Author(s):  
Constance Rinaldo
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Scientific Data ◽  
Text Search ◽  
Global Biodiversity Information Facility ◽  
The World ◽  
Species Specific ◽  
Program Interface ◽  
Biodiversity Information ◽  
Scientific Name

This will be a short introduction to the symposium: Improving access to hidden scientific data in the Biodiversity Heritage Library. The symposium will present examples of how the Biodiversity Heritage Library (BHL) collaborates across the international consortium and with community partners around the world to help enhance access to the biodiversity literature. Literature repositories, particularly the BHL collections, have been recognized as critical to the global scientific community. A diverse global user community propels BHL and BHL users to develop access tools beyond the standard “title, author, subject” search. BHL utilizes the Global Names Recognition and Discovery (GNRD) service to identify taxonomic names within text rendered by Optical Character Recognition (OCR) software, enabling scientific name searches and creation of species-specific bibliographies, critical to systematics research. In this symposium, we will hear from international partners and creative users making data from the BHL globally accessible for the kinds of larger-scale analysis enabled by BHL’s full-text search capabilities and Application Program Interface (API) protocols. In addition to taxonomic name services already incorporated in BHL, the consortium has also begun exploring georeferencing strategies for better searching and potential connections with key biodiversity resources such as the Global Biodiversity Information Facility (GBIF). With many different institutions around the world participating, the ability to work together virtually is critical for a seamless end product that meets the demands of the international community as well as the needs of local institutions.

scholarly journals An Efficient Language-Independent Multi-Font OCR for Arabic Script

2020 ◽  
Author(s):  
Hussein Osman ◽  
Karim Zaghw ◽  
Mostafa Hazem ◽  
Seifeldin Elsehely
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
State Of The Art ◽  
Recognition Task ◽  
Arabic Language ◽  
The State ◽  
Character Segmentation ◽  
Word Level ◽  
Digital Document ◽  
Arabic Ocr

Optical Character Recognition (OCR) is the process of extracting digitized text from images of scanned documents. While OCR systems have already matured in many languages, they still have shortcomings in cursive languages with overlapping letters such as the Arabic language. This paper proposes a complete Arabic OCR system that takes a scanned image of Arabic Naskh script as an input and generates a corresponding digital document. Our Arabic OCR system consists of the following modules: Pre-processing, Word-level Feature Extraction, Character Segmentation, Character Recognition, and Post-processing. This paper also proposes an improved font-independent character segmentation algorithm that outperforms the state-of-the-art segmentation algorithms. Lastly, the paper proposes a neural network model for the character recognition task. The system has experimented on several open Arabic corpora datasets with an average character segmentation accuracy 98.06%, character recognition accuracy 99.89%, and overall system accuracy 97.94% achieving outstanding results compared to the state-of-the-art Arabic OCR systems.

scholarly journals Bubble Captcha - A Start of the New Direction of Text Captcha Scheme Development

MENDEL ◽  
2017 ◽  
Vol 23 (1) ◽  
pp. 57-64 ◽  
Author(s):  
Ondrej Bostik ◽  
Karel Horak ◽  
Jan Klecka
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Turing Test ◽  
Simple Approach ◽  
Internet Services ◽  
Malicious Activity ◽  
Optical Character ◽  
Recognition Algorithms ◽  
The World ◽  
Human Capability

CAPTCHA, A Completely Automated Public Turing test to tell Computers and Humans Apart, iswell-known system widely used in all sorts of internet services around the world designated to secure the webfrom an automatic malicious activity. For almost two decades almost every system utilize a simple approach tothis problem containing a transcription of distorted letters from image to a text eld. The ground idea is to useimperfection of Optical Character Recognition algorithms against the computers. The development of OpticalCharacter recognition algorithms leads only to state, where the CAPTCHA schemes become more complex andhuman users have a great di culty with the transcription.This paper aims to present a new way of development of CAPTCHA schemes based more a human perception.The goal of this work is to implement new Captcha scheme and assess human capability to read unusual fontsnewer seen before.

scholarly journals An Efficient Algorithm for Estimating State Sequences in Imprecise Hidden Markov Models

2014 ◽  
Vol 50 ◽  
pp. 189-233 ◽  
Author(s):  
J. De Bock ◽  
G. De Cooman
Keyword(s):  
Hidden Markov Models ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Markov Models ◽  
Hidden Markov ◽  
Exact Algorithm ◽  
Mass Function ◽  
The State ◽  
Model Parameters ◽  
State Sequence

We present an efficient exact algorithm for estimating state sequences from outputs or observations in imprecise hidden Markov models (iHMMs). The uncertainty linking one state to the next, and that linking a state to its output, is represented by a set of probability mass functions instead of a single such mass function. We consider as best estimates for state sequences the maximal sequences for the posterior joint state model conditioned on the observed output sequence, associated with a gain function that is the indicator of the state sequence. This corresponds to and generalises finding the state sequence with the highest posterior probability in (precise-probabilistic) HMMs, thereby making our algorithm a generalisation of the one by Viterbi. We argue that the computational complexity of our algorithm is at worst quadratic in the length of the iHMM, cubic in the number of states, and essentially linear in the number of maximal state sequences. An important feature of our imprecise approach is that there may be more than one maximal sequence, typically in those instances where its precise-probabilistic counterpart is sensitive to the choice of prior. For binary iHMMs, we investigate experimentally how the number of maximal state sequences depends on the model parameters. We also present an application in optical character recognition, demonstrating that our algorithm can be usefully applied to robustify the inferences made by its precise-probabilistic counterpart.

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