This document provides a comprehensive survey of techniques for handwritten Hindi word recognition. It discusses the key stages in a handwritten word recognition system, including preprocessing, segmentation, feature extraction, classification, and post-processing. It then reviews several past studies on handwritten Hindi and Devanagari character recognition, comparing their different approaches to preprocessing, feature extraction, classification, and performance results. Finally, it provides a parametric evaluation of the techniques discussed and concludes that handwritten Hindi word recognition remains an active area of research with varying approaches.
An Optical Character Recognition for Handwritten Devanagari ScriptIJERA Editor
Optical Character Recognition is process of recognition of character from scanned document and lots of OCR now available in the market. But most of these systems work for Roman, Chinese, Japanese and Arabic characters . There are no sufficient number of work on Indian language script like Devanagari so this paper present a review on optical character recognition on handwritten Devanagari script
Devnagari handwritten numeral recognition using geometric features and statis...Vikas Dongre
This paper presents a Devnagari Numerical recognition method based on statistical
discriminant functions. 17 geometric features based on pixel connectivity, lines, line directions, holes,
image area, perimeter, eccentricity, solidity, orientation etc. are used for representing the numerals. Five
discriminant functions viz. Linear, Quadratic, Diaglinear, Diagquadratic and Mahalanobis distance are
used for classification. 1500 handwritten numerals are used for training. Another 1500 handwritten
numerals are used for testing. Experimental results show that Linear, Quadratic and Mahalanobis
discriminant functions provide better results. Results of these three Discriminants are fed to a majority
voting type Combination classifier. It is found that Combination classifier offers better results over
individual classifiers.
Performance Comparison between Different Feature Extraction Techniques with S...IJERA Editor
This paper represent the offline handwritten character recognition for Gurumukhi script. It is a major script of india. Many work has been done in many languages such as English , Chinese , Devanagri , Tamil etc. Gurumukhi is a script of Punjabi Language which is widely spoken across the globe. In this paper focus on better character recognition accuracy. The dataset include 7000 samples collected in different writing styles. These dataset divided in two set Training and Test. For Training set collect 5600 samples and 1400 as test set. The evaluated feature extraction include: Distance Profile, Diagonal feature and BDD(Background Direction Distribution). These features were classified by using SVM classifier. The Performance comparison have been made using one classifier with different feature extraction techniques. The experiment show that Diagonal feature extraction method has achieved highest recognition accuracy 95.39% than other features extraction method.
Recognition of Offline Handwritten Hindi Text Using SVMCSCJournals
Handwritten Hindi text recognition is emerging areas of research in the field of optical character recognition. In this paper, a segmentation based approach is used to recognize the text. The offline handwritten text is segmented into lines, lines into words and words into character for recognition. Shape features are extracted from the characters and fed into SVM classifier for recognition. The results obtained with the proposed feature set using SVM classifier is very challenging.
Optical character recognition is one of the emerging research topics in the field of image processing, and it has extensive area of application in pattern recognition. Odia handwritten script is the most research concern area because it has eldest and most likable language in the state of odisha, India. Odia character is a usually handwritten, which was generally occupied by scanner into machine readable form. In this regard several recognition technique have been evolved for variance kind of languages but writing pattern of odia character is just like as curve appearance; Hence it is more difficult for recognition. In this article we have presented the novel approach for Odia character recognition based on the different angle based symmetric axis feature extraction technique which gives high accuracy of recognition pattern. This empirical model generates a unique angle based boundary points on every skeletonised character images. These points are interconnected with each other in order to extract row and column symmetry axis. We extracted feature matrix having mean distance of row, mean angle of row, mean distance of column and mean angle of column from centre of the image to midpoint of the symmetric axis respectively. The system uses a 10 fold validation to the random forest (RF) classifier and SVM for feature matrix. We have considered the standard database on 200 images having each of 47 Odia character and 10 Odia numeric for simulation. As we have noted outcome of simulation of SVM and RF yields 96.3% and 98.2% accuracy rate on NIT Rourkela Odia character database and 88.9% and 93.6% from ISI Kolkata Odia numerical database.
An Optical Character Recognition for Handwritten Devanagari ScriptIJERA Editor
Optical Character Recognition is process of recognition of character from scanned document and lots of OCR now available in the market. But most of these systems work for Roman, Chinese, Japanese and Arabic characters . There are no sufficient number of work on Indian language script like Devanagari so this paper present a review on optical character recognition on handwritten Devanagari script
Devnagari handwritten numeral recognition using geometric features and statis...Vikas Dongre
This paper presents a Devnagari Numerical recognition method based on statistical
discriminant functions. 17 geometric features based on pixel connectivity, lines, line directions, holes,
image area, perimeter, eccentricity, solidity, orientation etc. are used for representing the numerals. Five
discriminant functions viz. Linear, Quadratic, Diaglinear, Diagquadratic and Mahalanobis distance are
used for classification. 1500 handwritten numerals are used for training. Another 1500 handwritten
numerals are used for testing. Experimental results show that Linear, Quadratic and Mahalanobis
discriminant functions provide better results. Results of these three Discriminants are fed to a majority
voting type Combination classifier. It is found that Combination classifier offers better results over
individual classifiers.
Performance Comparison between Different Feature Extraction Techniques with S...IJERA Editor
This paper represent the offline handwritten character recognition for Gurumukhi script. It is a major script of india. Many work has been done in many languages such as English , Chinese , Devanagri , Tamil etc. Gurumukhi is a script of Punjabi Language which is widely spoken across the globe. In this paper focus on better character recognition accuracy. The dataset include 7000 samples collected in different writing styles. These dataset divided in two set Training and Test. For Training set collect 5600 samples and 1400 as test set. The evaluated feature extraction include: Distance Profile, Diagonal feature and BDD(Background Direction Distribution). These features were classified by using SVM classifier. The Performance comparison have been made using one classifier with different feature extraction techniques. The experiment show that Diagonal feature extraction method has achieved highest recognition accuracy 95.39% than other features extraction method.
Recognition of Offline Handwritten Hindi Text Using SVMCSCJournals
Handwritten Hindi text recognition is emerging areas of research in the field of optical character recognition. In this paper, a segmentation based approach is used to recognize the text. The offline handwritten text is segmented into lines, lines into words and words into character for recognition. Shape features are extracted from the characters and fed into SVM classifier for recognition. The results obtained with the proposed feature set using SVM classifier is very challenging.
Optical character recognition is one of the emerging research topics in the field of image processing, and it has extensive area of application in pattern recognition. Odia handwritten script is the most research concern area because it has eldest and most likable language in the state of odisha, India. Odia character is a usually handwritten, which was generally occupied by scanner into machine readable form. In this regard several recognition technique have been evolved for variance kind of languages but writing pattern of odia character is just like as curve appearance; Hence it is more difficult for recognition. In this article we have presented the novel approach for Odia character recognition based on the different angle based symmetric axis feature extraction technique which gives high accuracy of recognition pattern. This empirical model generates a unique angle based boundary points on every skeletonised character images. These points are interconnected with each other in order to extract row and column symmetry axis. We extracted feature matrix having mean distance of row, mean angle of row, mean distance of column and mean angle of column from centre of the image to midpoint of the symmetric axis respectively. The system uses a 10 fold validation to the random forest (RF) classifier and SVM for feature matrix. We have considered the standard database on 200 images having each of 47 Odia character and 10 Odia numeric for simulation. As we have noted outcome of simulation of SVM and RF yields 96.3% and 98.2% accuracy rate on NIT Rourkela Odia character database and 88.9% and 93.6% from ISI Kolkata Odia numerical database.
Handwriting character recognition (HCR) is the ability of a computer to receive and interpret handwritten input. Handwritten Character Recognition is one of the active and challenging research areas in the field of Pattern Recognition. Pattern recognition is a process that taking in raw data and making an action based on the category of the pattern. HCR is one of the well-known applications of pattern recognition. Handwriting recognition especially for Indian languages is still in infant stage because not much work has been done it. This paper discuss about an idea to recognize Kannada vowels using chain code features. Kannada is a South Indian language. For any recognition system, an important part is feature extraction. A proper feature extraction method can increase the recognition ratio. In this paper, a chain code based feature extraction method is investigated for developing HCR system. Chain code is working based on 4-neighborhood or 8–neighborhood methods. Chain code is a sequence of code directions of a character and connection to a starting point which is often used in image processing. In this paper, 8–neighborhood method has been implemented which allows generation of eight different codes for each character. These codes have been used as features of the character image, which have been later on used for training and testing for K-Nearest Neighbor (KNN) classifiers. The level of accuracy reached to 100%.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
STRUCTURAL FEATURES FOR RECOGNITION OF HAND WRITTEN KANNADA CHARACTER BASED O...ijcseit
Research in image processing involves many active areas, of these Recognition of Handwritten character holds lots of promises and is challenging one .The idea is to enable the computer to be able to recognize intelligibly hand written inputs In this paper, a new method that uses structural features and support vector Machine (SVM) classifier for recognition of Handwritten Kannada characters is presented. On an average recognition accuracy of 89.84 % and 85.14% for handwritten Kannada vowels and Consonants obtained with this proposed method, inspite of inherent variations.
S TRUCTURAL F EATURES F OR R ECOGNITION O F H AND W RITTEN K ANNADA C ...ijcsit
Research in image processing involves many active a
reas, of these Recognition of Handwritten character
holds lots of promises and is challenging one .The
idea is to enable the computer to be able to recogn
ize
intelligibly hand written inputs In this paper, a
new method that uses structural features and suppo
rt
vector Machine (SVM) classifier for recognition of
Handwritten Kannada characters is presented. On an
average recognition accuracy of 89.84 % and 85.14%
for handwritten Kannada vowels and Consonants
obtained with this proposed method, inspite of inhe
rent variations
STRUCTURAL FEATURES FOR RECOGNITION OF HAND WRITTEN KANNADA CHARACTER BASED O...ijcseit
Research in image processing involves many active areas, of these Recognition of Handwritten character
holds lots of promises and is challenging one .The idea is to enable the computer to be able to recognize
intelligibly hand written inputs In this paper, a new method that uses structural features and support
vector Machine (SVM) classifier for recognition of Handwritten Kannada characters is presented. On an
average recognition accuracy of 89.84 % and 85.14% for handwritten Kannada vowels and Consonants
obtained with this proposed method, inspite of inherent variations.
STRUCTURAL FEATURES FOR RECOGNITION OF HAND WRITTEN KANNADA CHARACTER BASED O...ijcseit
Research in image processing involves many active areas, of these Recognition of Handwritten character
holds lots of promises and is challenging one .The idea is to enable the computer to be able to recognize
intelligibly hand written inputs In this paper, a new method that uses structural features and support
vector Machine (SVM) classifier for recognition of Handwritten Kannada characters is presented. On an
average recognition accuracy of 89.84 % and 85.14% for handwritten Kannada vowels and Consonants
obtained with this proposed method, inspite of inherent variations.
A Comprehensive Study On Handwritten Character Recognition Systemiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
International Journal of Research in Engineering and Science is an open access peer-reviewed international forum for scientists involved in research to publish quality and refereed papers. Papers reporting original research or experimentally proved review work are welcome. Papers for publication are selected through peer review to ensure originality, relevance, and readability.
Angular Symmetric Axis Constellation Model for Off-line Odia Handwritten Char...IJAAS Team
Optical character recognition is one of the emerging research topics in the field of image processing, and it has extensive area of application in pattern recognition. Odia handwritten script is the most research concern area because it has eldest and most likable language in the state of odisha, India. Odia character is a usually handwritten, which was generally occupied by scanner into machine readable form. In this regard several recognition technique have been evolved for variance kind of languages but writing pattern of odia character is just like as curve appearance; Hence it is more difficult for recognition. In this article we have presented the novel approach for Odia character recognition based on the different angle based symmetric axis feature extraction technique which gives high accuracy of recognition pattern. This empirical model generates a unique angle based boundary points on every skeletonised character images. These points are interconnected with each other in order to extract row and column symmetry axis. We extracted feature matrix having mean distance of row, mean angle of row, mean distance of column and mean angle of column from centre of the image to midpoint of the symmetric axis respectively. The system uses a 10 fold validation to the random forest (RF) classifier and SVM for feature matrix. We have considered the standard database on 200 images having each of 47 Odia character and 10 Odia numeric for simulation. As we have noted outcome of simulation of SVM and RF yields 96.3% and 98.2% accuracy rate on NIT Rourkela Odia character database and 88.9% and 93.6% from ISI Kolkata Odia numerical database.
Topographic Feature Extraction for Bengali and Hindi Character Imagessipij
Feature selection and extraction plays an important role in different classification based problems such as face recognition, signature verification, optical character recognition (OCR) etc. The performance of OCR highly depends on the proper selection and extraction of feature set. In this paper, we present novel features based on the topography of a character as visible from different viewing directions on a 2D plane. By topography of a character we mean the structural features of the strokes and their spatial relations. In this work we develop topographic features of strokes visible with respect to views from different directions (e.g. North, South, East, and West). We consider three types of topographic features: closed region, convexity of strokes, and straight line strokes. These features are represented as a shapebased graph which acts as an invariant feature set for discriminating very similar type characters efficiently. We have tested the proposed method on printed and handwritten Bengali and Hindi character images. Initial results demonstrate the efficacy of our approach.
Topographic Feature Extraction for Bengali and Hindi Character Imagessipij
Feature selection and extraction plays an important role in different classification based problems such as face recognition, signature verification, optical character recognition (OCR) etc. The performance of OCR highly depends on the proper selection and extraction of feature set. In this paper, we present novel features based on the topography of a character as visible from different viewing directions on a 2D plane. By topography of a character we mean the structural features of the strokes and their spatial relations. In this work we develop topographic features of strokes visible with respect to views from different directions (e.g. North, South, East, and West). We consider three types of topographic features: closed region, convexity of strokes, and straight line strokes. These features are represented as a shapebased graph which acts as an invariant feature set for discriminating very similar type characters efficiently. We have tested the proposed method on printed and handwritten Bengali and Hindi character images. Initial results demonstrate the efficacy of our approach.
Fragmentation of Handwritten Touching Characters in Devanagari ScriptZac Darcy
Character Segmentation of handwritten words is a difficult task because of different writing styles and
complex structural features. Segmentation of handwritten text in Devanagari script is an uphill task. The
occurrence of header line, overlapped characters in middle zone & half characters make the segmentation
process more difficultt. Sometimes, interline space and noise makes line fragmentation a difficult task.
Sometimes, interline space and noise makes line fragmentation a difficult task. Without separating the
touching characters, it will be difficult to identify the characters, hence fragmentation is necessary of the
touching characters in a word. So, we devised a technique, according to that first step will be
preprocessing of a word, than identify the joint points, form the bounding boxes around all vertical &
horizontal lines and finally fragment the touching characters on the basis of their height and width.
Fragmentation of handwritten touching characters in devanagari scriptZac Darcy
Character Segmentation of handwritten words is a difficult task because of different writing styles and
complex structural features. Segmentation of handwritten text in Devanagari script is an uphill task. The
occurrence of header line, overlapped characters in middle zone & half characters make the segmentation
process more difficultt. Sometimes, interline space and noise makes line fragmentation a difficult task.
Sometimes, interline space and noise makes line fragmentation a difficult task. Without separating the
touching characters, it will be difficult to identify the characters, hence fragmentation is necessary of the
touching characters in a word. So, we devised a technique, according to that first step will be
preprocessing of a word, than identify the joint points, form the bounding boxes around all vertical &
horizontal lines and finally fragment the touching characters on the basis of their height and width.
Handwriting character recognition (HCR) is the ability of a computer to receive and interpret handwritten input. Handwritten Character Recognition is one of the active and challenging research areas in the field of Pattern Recognition. Pattern recognition is a process that taking in raw data and making an action based on the category of the pattern. HCR is one of the well-known applications of pattern recognition. Handwriting recognition especially for Indian languages is still in infant stage because not much work has been done it. This paper discuss about an idea to recognize Kannada vowels using chain code features. Kannada is a South Indian language. For any recognition system, an important part is feature extraction. A proper feature extraction method can increase the recognition ratio. In this paper, a chain code based feature extraction method is investigated for developing HCR system. Chain code is working based on 4-neighborhood or 8–neighborhood methods. Chain code is a sequence of code directions of a character and connection to a starting point which is often used in image processing. In this paper, 8–neighborhood method has been implemented which allows generation of eight different codes for each character. These codes have been used as features of the character image, which have been later on used for training and testing for K-Nearest Neighbor (KNN) classifiers. The level of accuracy reached to 100%.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
STRUCTURAL FEATURES FOR RECOGNITION OF HAND WRITTEN KANNADA CHARACTER BASED O...ijcseit
Research in image processing involves many active areas, of these Recognition of Handwritten character holds lots of promises and is challenging one .The idea is to enable the computer to be able to recognize intelligibly hand written inputs In this paper, a new method that uses structural features and support vector Machine (SVM) classifier for recognition of Handwritten Kannada characters is presented. On an average recognition accuracy of 89.84 % and 85.14% for handwritten Kannada vowels and Consonants obtained with this proposed method, inspite of inherent variations.
S TRUCTURAL F EATURES F OR R ECOGNITION O F H AND W RITTEN K ANNADA C ...ijcsit
Research in image processing involves many active a
reas, of these Recognition of Handwritten character
holds lots of promises and is challenging one .The
idea is to enable the computer to be able to recogn
ize
intelligibly hand written inputs In this paper, a
new method that uses structural features and suppo
rt
vector Machine (SVM) classifier for recognition of
Handwritten Kannada characters is presented. On an
average recognition accuracy of 89.84 % and 85.14%
for handwritten Kannada vowels and Consonants
obtained with this proposed method, inspite of inhe
rent variations
STRUCTURAL FEATURES FOR RECOGNITION OF HAND WRITTEN KANNADA CHARACTER BASED O...ijcseit
Research in image processing involves many active areas, of these Recognition of Handwritten character
holds lots of promises and is challenging one .The idea is to enable the computer to be able to recognize
intelligibly hand written inputs In this paper, a new method that uses structural features and support
vector Machine (SVM) classifier for recognition of Handwritten Kannada characters is presented. On an
average recognition accuracy of 89.84 % and 85.14% for handwritten Kannada vowels and Consonants
obtained with this proposed method, inspite of inherent variations.
STRUCTURAL FEATURES FOR RECOGNITION OF HAND WRITTEN KANNADA CHARACTER BASED O...ijcseit
Research in image processing involves many active areas, of these Recognition of Handwritten character
holds lots of promises and is challenging one .The idea is to enable the computer to be able to recognize
intelligibly hand written inputs In this paper, a new method that uses structural features and support
vector Machine (SVM) classifier for recognition of Handwritten Kannada characters is presented. On an
average recognition accuracy of 89.84 % and 85.14% for handwritten Kannada vowels and Consonants
obtained with this proposed method, inspite of inherent variations.
A Comprehensive Study On Handwritten Character Recognition Systemiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
International Journal of Research in Engineering and Science is an open access peer-reviewed international forum for scientists involved in research to publish quality and refereed papers. Papers reporting original research or experimentally proved review work are welcome. Papers for publication are selected through peer review to ensure originality, relevance, and readability.
Angular Symmetric Axis Constellation Model for Off-line Odia Handwritten Char...IJAAS Team
Optical character recognition is one of the emerging research topics in the field of image processing, and it has extensive area of application in pattern recognition. Odia handwritten script is the most research concern area because it has eldest and most likable language in the state of odisha, India. Odia character is a usually handwritten, which was generally occupied by scanner into machine readable form. In this regard several recognition technique have been evolved for variance kind of languages but writing pattern of odia character is just like as curve appearance; Hence it is more difficult for recognition. In this article we have presented the novel approach for Odia character recognition based on the different angle based symmetric axis feature extraction technique which gives high accuracy of recognition pattern. This empirical model generates a unique angle based boundary points on every skeletonised character images. These points are interconnected with each other in order to extract row and column symmetry axis. We extracted feature matrix having mean distance of row, mean angle of row, mean distance of column and mean angle of column from centre of the image to midpoint of the symmetric axis respectively. The system uses a 10 fold validation to the random forest (RF) classifier and SVM for feature matrix. We have considered the standard database on 200 images having each of 47 Odia character and 10 Odia numeric for simulation. As we have noted outcome of simulation of SVM and RF yields 96.3% and 98.2% accuracy rate on NIT Rourkela Odia character database and 88.9% and 93.6% from ISI Kolkata Odia numerical database.
Topographic Feature Extraction for Bengali and Hindi Character Imagessipij
Feature selection and extraction plays an important role in different classification based problems such as face recognition, signature verification, optical character recognition (OCR) etc. The performance of OCR highly depends on the proper selection and extraction of feature set. In this paper, we present novel features based on the topography of a character as visible from different viewing directions on a 2D plane. By topography of a character we mean the structural features of the strokes and their spatial relations. In this work we develop topographic features of strokes visible with respect to views from different directions (e.g. North, South, East, and West). We consider three types of topographic features: closed region, convexity of strokes, and straight line strokes. These features are represented as a shapebased graph which acts as an invariant feature set for discriminating very similar type characters efficiently. We have tested the proposed method on printed and handwritten Bengali and Hindi character images. Initial results demonstrate the efficacy of our approach.
Topographic Feature Extraction for Bengali and Hindi Character Imagessipij
Feature selection and extraction plays an important role in different classification based problems such as face recognition, signature verification, optical character recognition (OCR) etc. The performance of OCR highly depends on the proper selection and extraction of feature set. In this paper, we present novel features based on the topography of a character as visible from different viewing directions on a 2D plane. By topography of a character we mean the structural features of the strokes and their spatial relations. In this work we develop topographic features of strokes visible with respect to views from different directions (e.g. North, South, East, and West). We consider three types of topographic features: closed region, convexity of strokes, and straight line strokes. These features are represented as a shapebased graph which acts as an invariant feature set for discriminating very similar type characters efficiently. We have tested the proposed method on printed and handwritten Bengali and Hindi character images. Initial results demonstrate the efficacy of our approach.
Fragmentation of Handwritten Touching Characters in Devanagari ScriptZac Darcy
Character Segmentation of handwritten words is a difficult task because of different writing styles and
complex structural features. Segmentation of handwritten text in Devanagari script is an uphill task. The
occurrence of header line, overlapped characters in middle zone & half characters make the segmentation
process more difficultt. Sometimes, interline space and noise makes line fragmentation a difficult task.
Sometimes, interline space and noise makes line fragmentation a difficult task. Without separating the
touching characters, it will be difficult to identify the characters, hence fragmentation is necessary of the
touching characters in a word. So, we devised a technique, according to that first step will be
preprocessing of a word, than identify the joint points, form the bounding boxes around all vertical &
horizontal lines and finally fragment the touching characters on the basis of their height and width.
Fragmentation of handwritten touching characters in devanagari scriptZac Darcy
Character Segmentation of handwritten words is a difficult task because of different writing styles and
complex structural features. Segmentation of handwritten text in Devanagari script is an uphill task. The
occurrence of header line, overlapped characters in middle zone & half characters make the segmentation
process more difficultt. Sometimes, interline space and noise makes line fragmentation a difficult task.
Sometimes, interline space and noise makes line fragmentation a difficult task. Without separating the
touching characters, it will be difficult to identify the characters, hence fragmentation is necessary of the
touching characters in a word. So, we devised a technique, according to that first step will be
preprocessing of a word, than identify the joint points, form the bounding boxes around all vertical &
horizontal lines and finally fragment the touching characters on the basis of their height and width.
Similar to Hand-written Hindi Word Recognition - A Comprehensive Survey (20)
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
NO1 Uk best vashikaran specialist in delhi vashikaran baba near me online vas...Amil Baba Dawood bangali
Contact with Dawood Bhai Just call on +92322-6382012 and we'll help you. We'll solve all your problems within 12 to 24 hours and with 101% guarantee and with astrology systematic. If you want to take any personal or professional advice then also you can call us on +92322-6382012 , ONLINE LOVE PROBLEM & Other all types of Daily Life Problem's.Then CALL or WHATSAPP us on +92322-6382012 and Get all these problems solutions here by Amil Baba DAWOOD BANGALI
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Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.