Character Segmentation in Gurumukhi Handwritten Textusing Hybrid Approach

Kumar, Rajiv; Singh, Amardeep

doi:10.7763/ijcte.2011.v3.357

Cited by 6 publications

(3 citation statements)

References 7 publications

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“…Rajiv Kumar and Amardeep Singh proposed Hybrid Approach for character segmentation in Gurumukhi handwritten text. In this algorithm [8], one part is extracted from the word present in the line. This extracted part is checked whether it has some meaningful symbol.…”

Section: Work Presented By Different Researchers For Segmentationmentioning

confidence: 99%

Segmentation of Marathi Handwritten Characters and Numerals

Khobragade¹

2015

IJIRCCE

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Segmentation is one the most important process which decides the success of character recognition technique. Segmentation is used to decompose an image of a sequence of characters into sub images of individual symbols by segmenting lines and words. In segmentation image is partitioned into multiple parts. With respect to the segmentation of handwritten words into characters it is a critical task because of complexity of structural features and varieties in writing styles. Due to this without segmentation these touching characters, it is difficult to recognize the individual characters, hence arises the need for segmentation of touching characters in a word. Here we consider Marathi words and Marathi Numerals for segmentation. The algorithm is use for Segmentation of lines and then characters. The segmented characters are then stores in result variable. First it Separate the lines and then it Separate the characters from the input image. This procedure is repeated till end of file.

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Section: Work Presented By Different Researchers For Segmentationmentioning

confidence: 99%

Segmentation of Marathi Handwritten Characters and Numerals

Khobragade¹

2015

IJIRCCE

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“…Convolutional Neural Networks (CNNs) are employed in a wide range of pattern recognition applications, including the analysis of medical images, paper documents, pictures, touch displays, and other devices [14]. Using CNNs for character recognition [15] both online and offline is a viable option.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Deep Learning Model with Interrelated Tagging Prototype with Segmentation for Telugu Optical Character Recognition

Dhanikonda

Sowjanya

Ramanaiah³

et al. 2022

Scientific Programming

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More than 66 million people in India speak Telugu, a language that dates back thousands of years and is widely spoken in South India. There has not been much progress reported on the advancement of Telugu text Optical Character Recognition (OCR) systems. Telugu characters can be composed of many symbols joined together. OCR is the process of turning a document image into a text-editable one that may be used in other applications. It saves a great deal of time and effort by not having to start from scratch each time. There are hundreds of thousands of different combinations of modifiers and consonants when writing compound letters. Symbols joined to one another form a compound character. Since there are so many output classes in Telugu, there’s a lot of interclass variation. Additionally, there are not any Telugu OCR systems that take use of recent breakthroughs in deep learning, which prompted us to create our own. When used in conjunction with a word processor, an OCR system has a significant impact on real-world applications. In a Telugu OCR system, we offer two ways to improve symbol or glyph segmentation. When it comes to Telugu OCR, the ability to recognise that Telugu text is crucial. In a picture, connected components are collections of identical pixels that are connected to one another by either 4- or 8-pixel connectivity. These connected components are known as glyphs in Telugu. In the proposed research, an efficient deep learning model with Interrelated Tagging Prototype with Segmentation for Telugu Text Recognition (ITP-STTR) is introduced. The proposed model is compared with the existing model and the results exhibit that the proposed model’s performance in text recognition is high.

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“…It is an operation that seeks to decompose an image of a sequence of character into sub images of individual symbols [11]. Character segmentation is an operation that seeks to decompose an image of a sequence of characters into subimages of individual symbols [12].…”

Section: Character Segmentationmentioning

confidence: 99%

An Automatic Number Plate Recognition System for Car Park Management

Mandi¹,

Kasamani²,

Raphael³

2017

IJCA

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Automatic Number Plate Recognition (ANPR) is an internationally recognized methodology that is used in vehicle identification. ANPR systems allow for real time recognition of a vehicle's number plate. Vehicle parking is an important component within any transportation system, whereby vehicles are often parked at destinations. With an increased number of motor vehicles on roads especially in developing countries, there is need for a vehicle identification mechanism that is effective, affordable and efficient. There are also increased insecurity challenges including terrorism which call for increased surveillance. In most academic institutions and car parks, the ongoing car park entry registration process for visitors, staff or students entering the institution involves a security guard having to confirm membership details by checking for membership sticker on the windscreen of the vehicle or by checking the driver's identification card. This process of writing is tedious and time consuming and is prone to inaccurate recordings, furthermore the backup and sharing of this vehicle information is difficult because the data is hard copy. We propose the adoption of a mobile based software solution that has ANPR capabilities to aid in vehicle identification and vehicle registration. The software application that was developed adopted an object oriented analysis and design methodology, the software developed implements Optical Character Recognition (OCR) using the mobile device camera to detect and capture the vehicle number plate. The proposed solution reduced registration time from 30 seconds to 6 seconds in addition to other benefits. It was recommended that the system be adopted and implemented to address the current challenges in vehicle registration and surveillance.

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Character Segmentation in Gurumukhi Handwritten Textusing Hybrid Approach

Cited by 6 publications

References 7 publications

Segmentation of Marathi Handwritten Characters and Numerals

Segmentation of Marathi Handwritten Characters and Numerals

An Efficient Deep Learning Model with Interrelated Tagging Prototype with Segmentation for Telugu Optical Character Recognition

An Automatic Number Plate Recognition System for Car Park Management

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