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The Origins, Evolution and Decline of the Khojki Script
The origins, evolution and decline of the Khojki script Juan Bruce The origins, evolution and decline of the Khojki script Juan Bruce Dissertation submitted in partial fulfilment of the requirements for the Master of Arts in Typeface Design, University of Reading, 2015. 5 Abstract The Khojki script is an Indian script whose origins are in Sindh (now southern Pakistan), a region that has witnessed the conflict between Islam and Hinduism for more than 1,200 years. After the gradual occupation of the region by Muslims from the 8th century onwards, the region underwent significant cultural changes. This dissertation reviews the history of the script and the different uses that it took on among the Khoja people since Muslim missionaries began their activities in Sindh communities in the 14th century. It questions the origins of the Khojas and exposes the impact that their transition from a Hindu merchant caste to a broader Muslim community had on the development of the script. During this process of transformation, a rich and complex creed, known as Satpanth, resulted from the blend of these cultures. The study also considers the roots of the Khojki writing system, especially the modernization that the script went through in order to suit more sophisticated means of expression. As a result, through recording the religious Satpanth literature, Khojki evolved and left behind its mercantile features, insufficient for this purpose. Through comparative analysis of printed Khojki texts, this dissertation examines the use of the script in Bombay at the beginning of the 20th century in the shape of Khoja Ismaili literature. -
BS-10 Text Part1.Cdr
Budget Sale 10 Auction of Coins, Tokens, Medals and Paper Money Sunday, 16th December, 2018, 11:00 am onwards Sonal Hall, Karve Road, Near Garware College Pune 411 004. Art & Antique Decor (Farokh S. Todywalla Proprietary Concern) [Antiques Licence No. 13A, Dt. 17/04/2006] Todywalla House, 80 Ardeshir Dady Street, Khetwadi, Mumbai 400 004. India Cell: +91-9820 054408 E-mail: [email protected] • Website: www.todywallaauctions.com Date of Sale: Sunday, 16th December, 2018, 11:00 am onwards Public View: Friday & Saturday 14th & 15th December, 11:00 am - 4:00 pm - At the venue By Appointment: 7th December to 13th December, 3 pm to 6 pm at Art & Antique Decor Todywalla House, 80 Ardeshir Dady Street, Khetwadi, Mumbai 400 004. India. Phone: +91-9820054408 Order of Sale: Ancient Coins................................................................... Lots 001 - 053 Hindu Coins of Medieval India ....................................... Lots 054 - 065 Sultanates ........................................................................ Lots 066 - 074 Mughals ........................................................................... Lots 075 - 157 Independent Kingdoms ................................................... Lots 158 - 168 Princely States ................................................................. Lots 169 - 189 Indo - Portuguese ............................................................ Lots 190 East India Company ......................................................... Lots 191 - 206 British India .................................................................... -
Gujarati Handwritten Numeral Optical Character Through Neural Network and Skeletonization
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Diponegoro University Institutional Repository GUJARATI HANDWRITTEN NUMERAL OPTICAL CHARACTER THROUGH NEURAL NETWORK AND SKELETONIZATION Kamal MORO*, Mohammed FAKIR, Badr Dine EL KESSAB, Belaid BOUIKHALENE, Cherki DAOUI (dont delete this line. It is used to insert authors detail) Abstract — This paper deals with an optical character recognition (OCR) system for handwritten Gujarati numbers. One may find so much of work for Indian languages like Hindi, Kannada, Tamil, Bangala, Malayalam, Gurumukhi etc, but Gujarati is a language for which hardly any work is Fig. 2 Confusing Gujarati digits traceable especially for handwritten characters. The features of Gujarati digits are abstracted by four different profiles of This paper addresses the problem of handwritten digits. Skeletonization and binarization are also done for Gujarati numeral recognition. Gujarati numeral preprocessing of handwritten numerals before their recognition requires binarization and skeletonozation as classification. This work has achieved approximately 80,5% of preprocess. Further, profiles are used for feature extraction success rate for Gujarati handwritten digit identification. and artificial neural network (ANN) is suggested for the classification. Index Terms —Optical character recognition, neural network, feature extraction, Gujarati handwritten digits, II. DATABASE skeletonization, classification. For handwritten English numerals, we have the CEDAR (Centre of Excellence for Document Analysis I. INTRODUCTION and Recognition at the University of New York at ujarati belonging to Devnagari family of Buffalo, USA) numeral database. It contains Glanguages, which originated and flourished in approximately 5000 samples of numerals. It contains Gujarat a western state of India, is spoken by over 50 approximately 5000 samples of numerals. -
Miramo Mmcomposer Reference Guide
Miramo® mmComposer mmComposer Reference Guide VERSION 1.5 Copyright © 2014–2018 Datazone Ltd. All rights reserved. Miramo®, mmChart™, mmComposer™ and fmComposer™ are trademarks of Datazone Ltd. All other trademarks are the property of their respective owners. Readers of this documentation should note that its contents are intended for guidance only, and do not constitute formal offers or undertakings. ‘License Agreement’ This software, called Miramo, is licensed for use by the user subject to the terms of a License Agreement between the user and Datazone Ltd. Use of this software outside the terms of this license agreement is strictly prohibited. Unless agreed otherwise, this License Agreement grants a non-exclusive, non-transferable license to use the software programs and related document- ation in this package (collectively referred to as Miramo) on licensed computers only. Any attempted sublicense, assignment, rental, sale or other transfer of the software or the rights or obligations of the License Agreement without prior written con- sent of Datazone shall be void. In the case of a Miramo Development License, it shall be used to develop applications only and no attempt shall be made to remove the associated watermark included in output documents by any method. The documentation accompanying this software must not be copied or re-distributed to any third-party in either printed, photocopied, scanned or electronic form. The software and documentation are copyrighted. Unless otherwise agreed in writ- ing, copies of the software may be made only for backup and archival purposes. Unauthorized copying, reverse engineering, decompiling, disassembling, and creating derivative works based on the software are prohibited. -
A Survey of Gujarati Handwritten Character Recognition Techniques
6 IX September 2018 International Journal for Research in Applied Science & Engineering Technology (IJRASET) ISSN: 2321-9653; IC Value: 45.98; SJ Impact Factor: 6.887 Volume 6 Issue IX, Sep 2018- Available at www.ijraset.com A Survey of Gujarati Handwritten Character Recognition Techniques Arpit A. Jain1, Harshal A. Arolkar2 Assistant Professor1, Associate Professor2 , GLS University Abstract: OCR termed as Optical Character Recognition, is a technique to convert mechanically or electronically an image, photo or scanned document of a handwritten or printed text into machine encoded text. HCR termed as Handwritten Character Recognition, is a form of OCR that is specifically designed to recognize the handwritten text. OCR and HCR nowadays are used extensively for information entry from printed or handwritten data records. In this paper we have done a survey on Gujarati Handwritten Character Recognition techniques. Keywords: OCR, Optical Character Recognition, HCR, Handwritten Character Recognition, Image Processing, Gujarati HCR, Gujarati Handwritten Character Recognition. I. INTRODUCTION Characters of any language are created using two types of mechanism namely; Digital and Handwritten Format. The digital characters are created with the help of a computer. The handwritten characters are the one’s that are written by person. Handwritten characters can further be classified into two categories: Offline and Online. The offline characters are written using any normal pen; while online characters are created using an optical pen or stylus on an electronic device. Figure 1 and Figure 2 shows the sample of offline and online characters. Figure 1: Offline Characters Figure 2: Online Characters ©IJRASET: All Rights are Reserved 461 International Journal for Research in Applied Science & Engineering Technology (IJRASET) ISSN: 2321-9653; IC Value: 45.98; SJ Impact Factor: 6.887 Volume 6 Issue IX, Sep 2018- Available at www.ijraset.com Languages like English, French, and Spanish have alphabets and vowels. -
Numbering Systems Developed by the Ancient Mesopotamians
Emergent Culture 2011 August http://emergent-culture.com/2011/08/ Home About Contact RSS-Email Alerts Current Events Emergent Featured Global Crisis Know Your Culture Legend of 2012 Synchronicity August, 2011 Legend of 2012 Wednesday, August 31, 2011 11:43 - 4 Comments Cosmic Time Meets Earth Time: The Numbers of Supreme Wholeness and Reconciliation Revealed In the process of writing about the precessional cycle I fell down a rabbit hole of sorts and in the process of finding my way around I made what I think are 4 significant discoveries about cycles of time and the numbers that underlie and unify cosmic and earthly time . Discovery number 1: A painting by Salvador Dali. It turns that clocks are not as bad as we think them to be. The units of time that segment the day into hours, minutes and seconds are in fact reconciled by the units of time that compose the Meso American Calendrical system or MAC for short. It was a surprise to me because one of the world’s foremost authorities in calendrical science the late Dr.Jose Arguelles had vilified the numbers of Western timekeeping as a most grievious error . So much so that he attributed much of the worlds problems to the use of the 12 month calendar and the 24 hour, 60 minute, 60 second day, also known by its handy acronym 12-60 time. I never bought into his argument that the use of those time factors was at fault for our largely miserable human-planetary condition. But I was content to dismiss mechanized time as nothing more than a convenient tool to facilitate the activities of complex societies. -
Recognition of Spoken Gujarati Numeral and Its Conversion Into Electronic Form
International Journal of Engineering Research & Technology (IJERT) ISSN: 2278-0181 Vol. 3 Issue 9, September- 2014 Recognition of Spoken Gujarati Numeral and Its Conversion into Electronic Form Bharat C. Patel Apurva A. Desai Smt. Tanuben & Dr. Manubhai Trivedi Dept. of Computer Science, Veer Narmad South Gujarat . College of information science, University, Surat, Gujarat, India Surat, Gujarat, India, Abstract— Speech synthesis and speech recognition are the area of interest for computer scientists. More and more A. Gujarati language researchers are working to make computer understand Gujarati is an Indo-Aryan language, descended from naturally spoken language. For International language like Sanskrit. Gujarati is the native language of the Indian state of English this technology has grown to a matured level. Here in this paper we present a model which recognize Gujarati TABLE I. PRONUNCIATION OF EQUIVALENT ENGLISH AND GUJARATI numeral spoken by speaker and convert it into machine editable NUMERALS. text of numeral. The proposed model makes use of Mel- Frequency Cepstral Coefficients (MFCC) as a feature set and K- English Pronunciation Gujarati Pronunciation Nearest Neighbor (K-NN) as classifier. The proposed model Digits Numerals 1 One Ek achieved average success rate of Gujarati spoken numeral is 2 Two Be about 78.13%. 3 Three Tran Keywords—speech recognition;MFCC; spoken Gujarati 4 Four Chaar numeral; KNN 5 Five Panch 6 Six Chha NTRODUCTION I. I 7 Seven Saat Speech recognition is a process in which a computer can 8 Eight Aath identify words or phrases spoken by different speakers in 9 Nine Nav different languages and translate them into a machineIJERTIJERT 0 Zero Shoonya readable-format. -
International Journal of Computer Sciences and Engineering Open Access Research Paper Vol.-6, Issue-9, Sept
International Journal of Computer Sciences and Engineering Open Access Research Paper Vol.-6, Issue-9, Sept. 2018 E-ISSN: 2347-2693 Online Handwritten Gujarati Numeral Recognition Using Support Vector Machine V. A. Naik1*, A. A. Desai2 1,2Department of Computer Science, Veer Narmad South Gujarat University, Surat, Gujarat, India * Corresponding Author: [email protected] Available online at: www.ijcseonline.org Accepted: 22/Sept/2018, Published: 30/Sept/2018 Abstract - In this paper, online handwritten numeral recognition for Gujarati is proposed. Online handwritten character recognition is in trend for research due to a rapid growth of handheld devices. The authors have compared Support Vector Machine (SVM) with linear, polynomial, and radial basis function kernels. The authors have used hybrid feature set. The authors have used zoning and chain code directional features which are extracted from each stroke. The dataset of the system is of 2000 samples and was collected by 200 writers and tested by 50 writers. The authors have achieved an accuracy of 92.60%, 95%, and 93.80% for linear, polynomial, RBF kernel and an average processing time of 0.13 seconds, 0.15seconds, and 0.18 seconds per stroke for linear, polynomial, RBF kernel. Keywords: Online Handwritten Character Recognition (OHCR), Handwritten Character Recognition (HCR), Optical Character Recognition (OCR), Support Vector Machine (SVM), Gujarati Numeral, Gujarati Digits I. INTRODUCTION There are many challenges in recognition of Gujarati digits because of variation in writing style and handwriting. Gujarati is an Indo-Aryan language and one of the official Gujarati digits have more curves than lines and there are languages of India, spoken by people of Gujarat state, union similar curves in some characters. -
Invariant Moments Approach for Gujarati Numerals
International Journal of Engineering and Applied Sciences (IJEAS) ISSN: 2394-3661, Volume-2, Issue-2, February 2015 Invariant Moments Approach for Gujarati Numerals Dr. Mamta Baheti characters in terms of lines, words and connected Abstract— Due to less reported work for Gujarati numerals components. By this effort, for connected component we have been motivated for same as Gujarati is a language not recognition rate was 78.34% for upper modifier recognition only of Indian states but widely spoken across world. We have. rate was 50% where as for lower modifier it was 77.55% and We have used noisy numerals for training and testing. Images for punctuation marks it was 29.6%, cumulative for overall it are pre-processed and then subjected to the proposed algorithm. was 72.3%. in our proposed algorithm we have used invariant moments as feature extraction technique and Gaussian distribution function In another work, Yajnik [5] had proposed an approach of as classifier. We found satisfactory results for some numerals. wavelet descriptors (Daubechies D4 wavelet coefficients) for The results can be improved by giving better quality images for image compression of printed Gujarati letters. They further training and testing. computed coefficients which were considered as an input to the recognizer (like nearest neighborhood or Neural Network Index Terms—Gujarati, Invariant moments,Gaussian architectures [6]-[7]) that reported them with results up to distribution function . 75% in compression. While reviewing literature, it was found that in 2005, Dholakia [8] have presented an algorithm to identify various zones. They have projected the use of I. INTRODUCTION horizontal and vertical profiles. -
Rudradaman I (Reign 130 AD – 150 AD)
Origins Scythians (referred to as Sakas in Indian sources) were a group of Iranian nomadic pastoral tribes. In the second century BC, central Asian nomadic tribes and tribes from the Chinese region invaded the region of present-day Kazakhstan whose inhabitants were Scythians. This promoted the Scythians to move towards Bactria and Parthia. After defeating the Parthian king, they moved towards India. Scythians who migrated to India are known as Indo-Scythians. The Sakas had an Indian kingdom larger than the Indo-Greeks. Maues (Reign 80 BC – 65 BC) Maues, also known as Moga was the earliest Indo-Scythian king. He ruled over Gandhara (present Pakistan and Afghanistan). He invaded the Indo-Greek territories but unsuccessfully. His capital was at Sirkap (Punjab, Pakistan). Many coins issued by Maues have been found. They contain Buddhist and also Hindu symbols. The languages used in these coins were Greek and Kharoshti. His son Azes I acquired the remaining Indo-Greek territories by defeating Hippostratos. Chastana (Reign 78 AD – 130 AD) He was a Saka ruler of the Western Kshatrapas (Satraps) dynasty who ruled over Ujjain. The Saka Era is believed to have started at his ascension to power in 78 AD. Ptolemy mentions him as “Tiasthenes” or “Testenes”. He was the founder of one of the two major Saka Kshatrapa dynasties in northwest India, the Bhadramukhas. The other dynasty was called Kshaharatas and included the king Nahapana (who was defeated by Satavahana king Gautamiputra Satakarni). Rudradaman I (Reign 130 AD – 150 AD) He is considered the greatest of the Saka rulers. He is from the Western Kshatrapa dynasty. -
Nuicone 2015)
2015 5th Nirma University International Conference on Engineering (NUiCONE 2015) Ahmedabad, India 26-28 November 2015 IEEE Catalog Number: CFP1555R-POD ISBN: 978-1-4799-9992-7 LIST OF FULL PAPERS ID No. Paper ID Title/Page No. Authors Analyzing effect of bad measurement data on load Jigar Patel, Daivat Desai, Vaibhav 38 555 flow and state estimation in power system 199 Patel, Dishang D. Trivedi and Santosh C. Vora VM Placement of Multidimensional Resources using Naisargi Patel and Govind Patel 39 350 Cartesian Co-ordinates Based Approach 205 Hierarchical Clustering Technique for Word Sense Nirali Patel, Bhargesh Patel, Rajvi 40 234 Disambiguation using Hindi WordNet 210 Parikh and Brijesh Bhatt Pose, Illumination and Expression Invariant Face Pradip Panchal, Palak Patel, 41 118 Recognition using Laplacian of Gaussian and Local Vandit Thakkar and Rachana Binary Pattern 215 Gupta Performance Enhancement of 12 X 160 Gbps (1.92 Rohit Patel and Dilip Kothari 42 519 Tbps) WDM Optical System for Transmission Distance upto 8000 km with Differential Coding 221 Arduino Controlled War Field Spy Robot using Jigneshkumar Patoliya, Haard 43 512 NightVision Wireless Camera and Android 227 Mehta and Hiteshkumar Patel Optimized Unscheduled Interchange Based Secondary Shital Pujara and Chetan Kotwal 44 310 Control for Two Area Deregulated Electricity Market 232 Introducing the Conceptual Model of Industrial Aarthi Raghavan 45 126 MOOCs (I-MOOCs) for Engineering Classes 240 Design Of Low Voltage Bandgap Reference Circuit Sushma Suresh Sangolli and 46 253 -
A Study and Comparative Analysis of Different Stemmer and Character Recognition Algorithms for Indian Gujarati Script
International Journal of Computer Applications (0975 – 8887) Volume 106 – No.2, November 2014 A Study and Comparative Analysis of Different Stemmer and Character Recognition Algorithms for Indian Gujarati Script Rajnish M. Rakholia Jatinderkumar R. Saini Ph.D PhD Scholar, R K University Associate Professor and Director I/C Bhavnagar Highway Narmada College of Computer Application Rajkot – Gujarat, India Bharuch – Gujarat, India ABSTRACT 1.1.2 Area below and above the Baseline: used A lot of work has been reported on optical character for below-base and above-base dependent vowels recognition for various non-Indian scripts like Chinese, respectively. English and Japanese and Indian scripts like Tamil, Hindi Telugu, etc., in this paper, we present a literature review on 1.1.3 Area before and after the Baseline: this is stemmer, optical character recognition (OCR) and Text the placeholder for consonants and independent vowels mining work on Indian scripts, mainly on the Gujarati [31]. languages. We have discussed the different techniques for OCR and text mining in Gujarati scripts, and summarized 2. STEMMER FOR GUJARATI SCRIPT most of the published work on this topic and gives future Stemming is the process to transform the words in texts into directions of research in the field of Indian script. their grammatical root form. Sheth J and Patel B (2014) suggested DHIYA a stemmer for General Terms Gujarati language, EMILLE corpus is used for training and Stemmer, Gujarati character recognition evaluation of the stemmer's performance. They obtained Keywords accuracy of 92.41% [26]. Classification, feature extraction, Gujarati script, Gujarati In (Sheth and Patel, 2012) they discussed different stemming stemmer, Indian script, pre-processing and segmentation.