scholarly journals Character Segmentation Of Degraded Odia Script

2020 ◽  
Vol 8 (2) ◽  
Author(s):  
Ipsita Pattnaik ◽  
Tushar Patnaik
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
West Bengal ◽  
Input Image ◽  
Character Segmentation ◽  
Vertical Projection ◽  
Projection Profile ◽  
Regional Language ◽  
Optical Character ◽  
Line Segmentation

Optical Character Recognition (OCR) is a field which converts printed text into computer understandable format that is editable in nature. Odia is a regional language used in Odisha, West Bengal & Jharkhand. It is used by over forty million people and still counting. With such large dependency on a language makes it important, to preserve its script, get a digital editable version of odia script. We propose a framework that takes computer printed odia script image as an input & gives a computer readable & user editable format of same, which eventually recognizes the characters printed in input image. The system uses various techniques to improve the image & perform Line segmentation followed by word segmentation & finally character segmentation using horizontal & vertical projection profile.

scholarly journals Word and Chracter Segmentation in Devnagari and Odia Script – A Comparitive Analysis

2020 ◽  
Vol 9 (9) ◽  
pp. 377-382
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Research Area ◽  
Character Segmentation ◽  
National Language ◽  
Eastern India ◽  
Regional Language ◽  
Optical Character ◽  
Active Research ◽  
Active Research Area

Optical Character Recognition has been an active research area in computer science for several years. Several research works undertaken on various languages in India. In this paper an attempt has been made to find out the percentage of accuracy in word and character segmentation of Hindi (National language of India) and Odia is one of the Regional Language mostly spoken in Odisha and a few Eastern India states. A comparative article has been published under this article. 10 sets of each printed Odia and Devanagari scripts with different word limits were used in this study. The documents were scanned at 300dpi before adopting pre-processing and segmentation procedure. The result shows that the percentage of accuracy both in word and character segmentation is higher in Odia language as compared to Hindi language. One of the reasons is the use of headers line in Hindi which makes the segmentation process cumbersome. Thus, it can be concluded that the accuracy level can vary from one language to the other and from word segmentation to that of the character segmentation.

scholarly journals Line Segmentation Challenges in Tamil Language Palm Leaf Manuscripts

2019 ◽  
Vol 9 (1) ◽  
pp. 2363-2367
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Character Segmentation ◽  
Binary Images ◽  
Optical Character ◽  
Colour Image ◽  
Segmentation Methods ◽  
Segmentation Algorithms ◽  
Line Segmentation ◽  
Palm Leaf

The process of an Optical Character Recognition (OCR) for ancient hand written documents or palm leaf manuscripts is done by means of four phases. The four phases are ‘line segmentation’, ‘word segmentation’, ‘character segmentation’, and ‘character recognition’. The colour image of palm leaf manuscripts are changed into binary images by using various pre-processing methods. The first phase of an OCR might break through the hurdles of touching lines and overlapping lines. The character recognition becomes futile when the line segmentation is erroneous. In Tamil language palm leaf manuscript recognition, there are only a handful of line segmentation methods. Moreover, the available methods are not viable to meet the required standards. This article is proposed to fill the lacuna in terms of the methods necessary for line segmentation in Tamil language document analysis. The method proposed compares its efficiency with the line segmentation algorithms work on binary images such as the Adaptive Partial Projection (APP) and A* Path Planning (A*PP). The tools and criteria of evaluation metrics are measured from ICDAR 2013 Handwriting Segmentation Contest.

scholarly journals Automate Identification and Recognition of Handwritten Text from an Image

2021 ◽  
Vol 12 (3) ◽  
pp. 3800-3808
Author(s):  
Siddharth Salar Et.al
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Input Image ◽  
Character Segmentation ◽  
Single Image ◽  
Optical Character ◽  
Handwritten Text ◽  
Printed Text ◽  
Test Picture ◽  
Written Explanations

Handwritten text acknowledgment is yet an open examination issue in the area of Optical Character Recognition (OCR). This paper proposes a productive methodology towards the advancement of handwritten text acknowledgment frameworks. The primary goal of this task is to create AI calculation to empower element and information extraction from records with manually written explanations, with an, expect to distinguish transcribed words on a picture. The main aim of this project is to extract text, this text can be handwritten text or it can machine printed text and convert it into computer understandable or wNe can say computer editable format. To implement thais project we have used PyTesseract which is an open-sourcemOCR engine used to recognize handwritten text and OpenCV a library in python used to solve computer vision problems. So the input image is executed in various steps, first there is pre-processing of an image then there is text localization after that there is character segmentation and character recognition and finally we have post-processing               of image. Further image processingalgorithms can also be used to deal with the multiple characters input in a single image, tilt image, or rotated image. The prepared framework gives a normal precision of more than 95 % with the concealed test picture.

scholarly journals Character Segmentation and Skew Correction for Handwritten Devanagari Scripts: A Friends Technique

2019 ◽  
Vol 8 (1) ◽  
pp. 50-54
Author(s):  
Ashok Kumar Bathla . ◽  
Sunil Kumar Gupta .
Keyword(s):  
Human Brain ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Character Segmentation ◽  
Novel Technique ◽  
Skew Correction ◽  
Optical Character ◽  
Handwritten Text ◽  
Scripting Language ◽  
The Way

Optical Character Recognition (OCR) technology allows a computer to “read” text (both typed and handwritten) the way a human brain does.Significant research efforts have been put in the area of Optical Character Segmentation (OCR) of typewritten text in various languages, however very few efforts have been put on the segmentation and skew correction of handwritten text written in Devanagari which is a scripting language of Hindi. This paper aims a novel technique for segmentation and skew correction of hand written Devanagari text. It shows the accuracy of 91% and takes less than one second to segment a particular handwritten word.

scholarly journals Barrier Access Control Using Sensors Platform and Vehicle License Plate Characters Recognition

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 3015 ◽  
Author(s):  
Farman Ullah ◽  
Hafeez Anwar ◽  
Iram Shahzadi ◽  
Ata Ur Rehman ◽  
Shizra Mehmood ◽  
...  
Keyword(s):  
Access Control ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Character Segmentation ◽  
License Plate ◽  
License Plate Recognition ◽  
Ultrasonic Sensors ◽  
Optical Character

The paper proposes a sensors platform to control a barrier that is installed for vehicles entrance. This platform is automatized by image-based license plate recognition of the vehicle. However, in situations where standardized license plates are not used, such image-based recognition becomes non-trivial and challenging due to the variations in license plate background, fonts and deformations. The proposed method first detects the approaching vehicle via ultrasonic sensors and, at the same time, captures its image via a camera installed along with the barrier. From this image, the license plate is automatically extracted and further processed to segment the license plate characters. Finally, these characters are recognized with the help of a standard optical character recognition (OCR) pipeline. The evaluation of the proposed system shows an accuracy of 98% for license plates extraction, 96% for character segmentation and 93% for character recognition.

scholarly journals Comparative Study of Text Line Segmentation Algorithms on Low Quality Documents

2013 ◽  
pp. 270-276
Author(s):  
P. Soujanya ◽  
Vijaya Kumar Koppula ◽  
Kishore Gaddam
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Recognition System ◽  
Character Segmentation ◽  
Text Line ◽  
Run Length ◽  
Processing Step ◽  
Segmentation Algorithms ◽  
Text Line Segmentation ◽  
Line Segmentation

Segmentation of text lines is one of the important steps in the Optical Character Recognition system. Text Line Segmentation is pre-processing step of word and character segmentation. Text Line Segmentation can be viewed simple for printing documents which contains distinct spaces between the lines. And it is more complex for the documents where text lines are overlap, touch, curvilinear and variation of space between text lines like in Telugu scripts and skewed documents. The main objective of this project is to investigate different text line segmentation algorithms like Projection Profiles, Run length smearing and Adaptive Run length smearing on low quality documents. These methods are experimented and compare their accuracy and results.

scholarly journals Recognition of Hindi and Bengali Handwritten and Typed Text from Images using Tesseract on Android Platform

2019 ◽  
Vol 9 (1) ◽  
pp. 3507-3516
Keyword(s):  
Data Storage ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Input Image ◽  
Word Formation ◽  
Character Segmentation ◽  
Data Conversion ◽  
Data Sets ◽  
Handwritten Text ◽  
Android Platform

The concept of digitization has marked a revolution in the area of data conversion, data storage and data sharing by converting non-editable typographic & handwritten text into editable electronic text. Though numerous such works have been carried out across the world in various languages using Optical Character Recognition (OCR), satisfactory output has been observed only in a few languages. This paper is an endeavor towards taking a step ahead in the digitization of two of the most extensively spoken languages in the Indian sub-continent – Hindi and Bengali - using Google’s open source OCR Engine, Tesseract. Working on the scripts of these two languages of Brahmi origin has its own challenges owing to their varied traits of character segmentation and word formation. Here, the training of Tesseract with data sets of Hindi and Bengali typographic and handwritten characters has been integrated with an inimitable pre-processing stage involving input image customization and image augmentation that significantly enhances the image quality allowing Tesseract to offer more accurate results, especially in cases of handwritten texts and obscure images. Besides, it also incorporates the features of English translation and text to speech translation which render their significance among the non-natives and visually impaired mass. The focal idea of this paper has been to reach out to an extended mass by enabling digitization on the Android platform. Comparative analysis carried out on three distinctive parameters - on images with typographic texts, handwritten texts and on inferior quality images - shows that the paper, to a certain extent, does succeed in projecting superior output in at least two cases as compared to the most consistent Android application of today’s time.

scholarly journals AUTOMATICS NUMBER PLATE RECOGNITION USING CONVOLUTION NEURAL NETWORK

2020 ◽  
Vol 3 (2) ◽  
pp. 234-244
Author(s):  
Siddhartha Roy ◽  
Keyword(s):  
Real Time ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Rapid Development ◽  
Red Light ◽  
Training Needs ◽  
Character Segmentation ◽  
License Plate ◽  
Training Set ◽  
Optical Character

In the last few years, Automatic Number Plate Recognition (ANPR) systems have become widely used for security, safety, and also commercial aspects such as parking control access, and legal steps for the red light violation, highway speed detection, and stolen vehicle detection. The license plate of any vehicle contains a number of numeric characters recognized by the computer. Each country in the world has specific characteristics of the license plate. Due to rapid development in the information system field, the previous manual license plate number writing process in the database is replaced by special intelligent device in a real-time environment. Several approaches and techniques are exploited to achieve better systems accuracy and real-time execution. It is a process of recognizing number plates using Optical Character Recognition (OCR) on images. This paper proposes a deep learning-based approach to detect and identify the Indian number plate automatically. It is based on new computer vision algorithms of both number plate detection and character segmentation. The training needs several images to obtain greater accuracy. Initially, we have developed a training set database by training different segmented characters. Several tests were done by varying the Epoch value to observe the change of accuracy. The accuracy is more than 95% that presents an acceptable value compared to related works, which is quite satisfactory and recognizes the blurred number plate.

scholarly journals Segmentation of Handwritten Text Document Written in Devanagri Script for Simple character, skewed character and broken character

2013 ◽  
Vol 8 (1) ◽  
pp. 686-691
Author(s):  
Vneeta Rani ◽  
Dr.Vijay Laxmi
Keyword(s):  
Artificial Intelligence ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Character Segmentation ◽  
Research Areas ◽  
Text Document ◽  
Optical Character ◽  
Handwritten Text ◽  
Recognition Phase ◽  
Simple Character

OCR (optical character recognition) is a technology that is commonly used for recognizing patterns artificial intelligence & computer machine. With the help of OCR we can convert scanned document into editable documents which can be further used in various research areas. In this paper, we are presenting a character segmentation technique that can segment simple characters, skewed characters as well as broken characters. Character segmentation is very important phase in any OCR process because output of this phase will be served as input to various other phase like character recognition phase etc. If there is some problem in character segmentation phase then recognition of the corresponding character is very difficult or nearly impossible.

scholarly journals Kurdish Optical Character Recognition

2018 ◽  
Vol 2 (1) ◽  
pp. 18-27
Author(s):  
Rasty Yaseen ◽  
Hossein Hassani
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Character Segmentation ◽  
Accuracy Rate ◽  
Optical Character ◽  
Arabic Script

Currently, no offline tool is available for Optical Character Recognition (OCR) in Kurdish. Kurdish is spoken in different dialects and uses several scripts for writing. The Persian/Arabic script is widely used among these dialects. The Persian/Arabic script is written from Right to Left (RTL), it is cursive, and it uses unique diacritics. These features, particularly the last two, affect the segmentation stage in developing a Kurdish OCR. In this article, we introduce an enhanced character segmentation based method which addresses the mentioned characteristics. We applied the method to text-only images and tested the Kurdish OCR using documents of different fonts, font sizes, and image resolutions. The results of the experiments showed that the accuracy rate of character recognition of the proposed method was 90.82% on average.

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