This document is a project proposal for an OCR algorithm for Ge'ez characters. It aims to build a system to identify and digitize Ethiopian documents to preserve intellectual property and make it accessible digitally. The proposed system would use OpenCV and neural networks to recognize Ge'ez fonts in scanned documents and convert them to editable text files. It would train on Ge'ez, Amharic and Tigrigna words to recognize and correct errors in characters. The methodology involves preprocessing scans, segmenting text from images, extracting character features, recognizing words, and outputting final text files. The schedule plans for data collection, requirements, design, implementation and results over 3-4 months.
This document proposes developing an optical character recognition (OCR) algorithm for Ge'ez characters. It begins with an introduction to the Ge'ez script and its use in Ethiopia and Eritrea. It then discusses what OCR is, when and why it is used, and the problem of digitizing Ethiopia's historical documents written in Ge'ez. The authors propose using OCR technology and artificial neural networks to develop an application that can recognize Ge'ez characters from images. Such an algorithm could be used to digitize important religious and cultural documents currently only available on paper, helping preserve Ethiopia's intellectual property. Results and conclusion sections indicate the algorithm would make digitization of Ge'ez texts more feasible while a
Natural language processing with python and amharic syntax parse tree by dani...Daniel Adenew
Natural Language Processing is an interrelated disincline adding the capability of communicating as human beings to Computerworld. Amharic language is having much improvement over time thanks to researcher at PHD, MSC level at AAU. Here , I have tried to study and come up a limited scope solution that does syntax parsing for Amharic language and draws syntax parse trees using Python!!
Natural Language Processing (NLP) is a field of artificial intelligence that deals with interactions between computers and humans using natural language. NLP techniques allow computers to understand, analyze, and generate human languages to accomplish useful tasks. The goal of NLP is for computers to accurately understand and interpret human speech and text. NLP is a challenging problem because human language is complex, context-dependent and ambiguous.
Natural Language Processing and Machine LearningKarthik Sankar
The document provides an introduction to natural language processing and machine learning. It discusses how NLP is concerned with interactions between computers and human languages. It describes the categories of NLP including phonology, morphology, syntax and semantics. It also discusses machine learning approaches like supervised and unsupervised learning, and symbolic learning frameworks involving concept spaces, operations and heuristic search.
Different valuable tools for Arabic sentiment analysis: a comparative evaluat...IJECEIAES
Arabic Natural language processing (ANLP) is a subfield of artificial intelligence (AI) that tries to build various applications in the Arabic language like Arabic sentiment analysis (ASA) that is the operation of classifying the feelings and emotions expressed for defining the attitude of the writer (neutral, negative or positive). In order to work on ASA, researchers can use various tools in their research projects without explaining the cause behind this use, or they choose a set of libraries according to their knowledge about a specific programming language. Because of their libraries' abundance in the ANLP field, especially in ASA, we are relying on JAVA and Python programming languages in our research work. This paper relies on making an in-depth comparative evaluation of different valuable Python and Java libraries to deduce the most useful ones in Arabic sentiment analysis (ASA). According to a large variety of great and influential works in the domain of ASA, we deduce that the NLTK, Gensim and TextBlob libraries are the most useful for Python ASA task. In connection with Java ASA libraries, we conclude that Weka and CoreNLP tools are the most used, and they have great results in this research domain.
Natural language processing provides a way in which human interacts with computer / machines by means of voice.
"Google Search by voice is the best example " which makes use of natural language processing.
Natural Language Processing (NLP) is often taught at the academic level from the perspective of computational linguists. However, as data scientists, we have a richer view of the world of natural language - unstructured data that by its very nature has important latent information for humans. NLP practitioners have benefitted from machine learning techniques to unlock meaning from large corpora, and in this class we’ll explore how to do that particularly with Python, the Natural Language Toolkit (NLTK), and to a lesser extent, the Gensim Library.
NLTK is an excellent library for machine learning-based NLP, written in Python by experts from both academia and industry. Python allows you to create rich data applications rapidly, iterating on hypotheses. Gensim provides vector-based topic modeling, which is currently absent in both NLTK and Scikit-Learn. The combination of Python + NLTK means that you can easily add language-aware data products to your larger analytical workflows and applications.
This document proposes developing an optical character recognition (OCR) algorithm for Ge'ez characters. It begins with an introduction to the Ge'ez script and its use in Ethiopia and Eritrea. It then discusses what OCR is, when and why it is used, and the problem of digitizing Ethiopia's historical documents written in Ge'ez. The authors propose using OCR technology and artificial neural networks to develop an application that can recognize Ge'ez characters from images. Such an algorithm could be used to digitize important religious and cultural documents currently only available on paper, helping preserve Ethiopia's intellectual property. Results and conclusion sections indicate the algorithm would make digitization of Ge'ez texts more feasible while a
Natural language processing with python and amharic syntax parse tree by dani...Daniel Adenew
Natural Language Processing is an interrelated disincline adding the capability of communicating as human beings to Computerworld. Amharic language is having much improvement over time thanks to researcher at PHD, MSC level at AAU. Here , I have tried to study and come up a limited scope solution that does syntax parsing for Amharic language and draws syntax parse trees using Python!!
Natural Language Processing (NLP) is a field of artificial intelligence that deals with interactions between computers and humans using natural language. NLP techniques allow computers to understand, analyze, and generate human languages to accomplish useful tasks. The goal of NLP is for computers to accurately understand and interpret human speech and text. NLP is a challenging problem because human language is complex, context-dependent and ambiguous.
Natural Language Processing and Machine LearningKarthik Sankar
The document provides an introduction to natural language processing and machine learning. It discusses how NLP is concerned with interactions between computers and human languages. It describes the categories of NLP including phonology, morphology, syntax and semantics. It also discusses machine learning approaches like supervised and unsupervised learning, and symbolic learning frameworks involving concept spaces, operations and heuristic search.
Different valuable tools for Arabic sentiment analysis: a comparative evaluat...IJECEIAES
Arabic Natural language processing (ANLP) is a subfield of artificial intelligence (AI) that tries to build various applications in the Arabic language like Arabic sentiment analysis (ASA) that is the operation of classifying the feelings and emotions expressed for defining the attitude of the writer (neutral, negative or positive). In order to work on ASA, researchers can use various tools in their research projects without explaining the cause behind this use, or they choose a set of libraries according to their knowledge about a specific programming language. Because of their libraries' abundance in the ANLP field, especially in ASA, we are relying on JAVA and Python programming languages in our research work. This paper relies on making an in-depth comparative evaluation of different valuable Python and Java libraries to deduce the most useful ones in Arabic sentiment analysis (ASA). According to a large variety of great and influential works in the domain of ASA, we deduce that the NLTK, Gensim and TextBlob libraries are the most useful for Python ASA task. In connection with Java ASA libraries, we conclude that Weka and CoreNLP tools are the most used, and they have great results in this research domain.
Natural language processing provides a way in which human interacts with computer / machines by means of voice.
"Google Search by voice is the best example " which makes use of natural language processing.
Natural Language Processing (NLP) is often taught at the academic level from the perspective of computational linguists. However, as data scientists, we have a richer view of the world of natural language - unstructured data that by its very nature has important latent information for humans. NLP practitioners have benefitted from machine learning techniques to unlock meaning from large corpora, and in this class we’ll explore how to do that particularly with Python, the Natural Language Toolkit (NLTK), and to a lesser extent, the Gensim Library.
NLTK is an excellent library for machine learning-based NLP, written in Python by experts from both academia and industry. Python allows you to create rich data applications rapidly, iterating on hypotheses. Gensim provides vector-based topic modeling, which is currently absent in both NLTK and Scikit-Learn. The combination of Python + NLTK means that you can easily add language-aware data products to your larger analytical workflows and applications.
This presentation will help you to understand the basic concepts of Natural Language Processing With this you will understand the significance of Natural Language Processing in our daily life
This document provides an outline on natural language processing and machine vision. It begins with an introduction to different levels of natural language analysis, including phonetic, syntactic, semantic, and pragmatic analysis. Phonetic analysis constructs words from phonemes using frequency spectrograms. Syntactic analysis builds a structural description of sentences through parsing. Semantic analysis generates a partial meaning representation from syntax, while pragmatic analysis uses context. The document also introduces machine vision as a technology using optical sensors and cameras for industrial quality control through detection of faults. It operates through sensing images, processing/analyzing images, and various applications.
Stemming And Lemmatization Tutorial | Natural Language Processing (NLP) With ...Edureka!
( **Natural Language Processing Using Python: - https://www.edureka.co/python-natural... ** )
This PPT will provide you with detailed and comprehensive knowledge of the two important aspects of Natural Language Processing ie. Stemming and Lemmatization. It will also provide you with the differences between the two with Demo on each. Following are the topics covered in this PPT:
Introduction to Big Data
What is Text Mining?
What is NLP?
Introduction to Stemming
Introduction to Lemmatization
Applications of Stemming & Lemmatization
Difference between stemming & Lemmatization
Follow us to never miss an update in the future.
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The document discusses natural language and natural language processing (NLP). It defines natural language as languages used for everyday communication like English, Japanese, and Swahili. NLP is concerned with enabling computers to understand and interpret natural languages. The summary explains that NLP involves morphological, syntactic, semantic, and pragmatic analysis of text to extract meaning and understand context. The goal of NLP is to allow humans to communicate with computers using their own language.
Natural Language Processing(NLP) is a subset Of AI.It is the ability of a computer program to understand human language as it is spoken.
Contents
What Is NLP?
Why NLP?
Levels In NLP
Components Of NLP
Approaches To NLP
Stages In NLP
NLTK
Setting Up NLP Environment
Some Applications Of NLP
Natural Language Processing (NLP) is a subfield of artificial intelligence that aims to help computers understand human language. NLP involves analyzing text at different levels, including morphology, syntax, semantics, discourse, and pragmatics. The goal is to map language to meaning by breaking down sentences into syntactic structures and assigning semantic representations based on context. Key steps include part-of-speech tagging, parsing sentences into trees, resolving references between sentences, and determining intended meaning and appropriate actions. Together, these allow computers to interpret and respond to natural human language.
This document provides an overview of various topics in natural language processing including speech recognition, natural language understanding, natural language generation, chatbots, and machine translation. It discusses key aspects of each topic such as how speech recognition works, the challenges of natural language understanding, and how machine translation systems have evolved to consider context and domain specificity.
Its started off as a part of Artificial intelligence. NLP is challenging , but its been widely researched for future application which will have human touch.
Natural Language Processing: Definition and ApplicationStephen Shellman
Steve Shellman heads Strategic Analysis Enterprises, Inc., an organization that uses academic methodologies and complex techniques such as named-entity extraction and natural language processing to provide innovative solutions for strategic planning. Natural language processing (NLP) began in the 1950s in intelligence and automatic translation and concerns language interactions between computers and humans, allowing computers to understand human speech in real-time, though speech contains ambiguity. Currently, NLP uses machine learning to examine patterns and expand comprehension, being applied to fields like named-entity extraction, deep analytics, and opinion mining.
Natural language processing provides a way in which human interacts with computer / machines by means of voice.
"Google Search by voice is the best example " which makes use of natural language processing..
Our speech to text conversion project aims to help the nearly 20% of people worldwide with disabilities by allowing them to control their computer and share information using only their voice. The system uses acoustic and language models with a speech engine to recognize speech and convert it to text. It can perform operations like opening calculator and wordpad. Speech recognition has applications in areas like cars, healthcare, education and daily life. Accuracy depends on factors like vocabulary size, speaker dependence, and speech type (isolated, continuous). The system aims to improve accessibility while reducing costs.
This document discusses speech synthesis technology. It begins with an introduction defining speech synthesis as the artificial production of human speech. It then discusses the history of speech synthesis, including early inventions and developments of speech synthesizers. It also covers the construction and various approaches to speech synthesis, such as concatenative synthesis and formant synthesis. The document concludes by discussing applications of speech synthesis and remaining challenges.
Past, Present, and Future: Machine Translation & Natural Language Processing ...John Tinsley
This was a presentation given at the European Patent Office's annual Patent Information Conference in Madrid, Spain on November 10th, 2016.
In it, we give an overview of how machine translation works, latest advances in neural MT, and how this can be applied to patents and intellectual property content, not only for translations but also information extraction and other NLP applications.
SCRIPTS AND NUMERALS IDENTIFICATION FROM PRINTED MULTILINGUAL DOCUMENT IMAGEScscpconf
This document presents a technique for identifying scripts (Tamil, English, Hindi) and numerals from multilingual document images using a rule-based classifier. Words are segmented and the first character of each word is represented as a 9-bit vector based on features like density, shape, and transitions. A rule-based classifier containing rules derived from training data is used to classify the script of each character. The technique aims to automatically categorize multilingual documents before applying optical character recognition and requires minimal preprocessing with high accuracy.
Natural Language Processing (NLP) - IntroductionAritra Mukherjee
This presentation provides a beginner-friendly introduction towards Natural Language Processing in a way that arouses interest in the field. I have made the effort to include as many easy to understand examples as possible.
Natural language processing (NLP) analyzes and represents natural language text or speech at linguistic levels to achieve human-like language processing for applications. NLP was influenced by Turing's 1950 paper on machine intelligence and involved early systems like SHRDLU in the 1960s. NLP understands, generates, and integrates natural language through techniques like morphological, syntactic, semantic and discourse analysis to benefit domains like search, translation, sentiment analysis, social media and more.
This document discusses text analysis tools and techniques. It provides an overview of 8 popular text analysis tools, including MonkeyLearn, Aylien, IBM Watson, Thematic, Google Cloud NLP, Amazon Comprehend, MeaningCloud, and Lexalytics. It then describes 5 common text analysis techniques: information extraction, categorization, clustering, visualization, and summarization. Finally, it outlines 7 basic functions of text analytics: language identification, tokenization, sentence breaking, part-of-speech tagging, chunking, syntax parsing, and sentence chaining.
Natural language processing (NLP) is a subfield of artificial intelligence that aims to allow computers to understand human language. NLP involves analyzing and representing text or speech at different linguistic levels for applications like question answering or machine translation. Challenges for NLP include ambiguities in language like lexical, syntactic, semantic, and anaphoric ambiguities. Common NLP tasks include part-of-speech tagging, parsing, named entity recognition, and sentiment analysis. Applications of NLP include text processing, machine translation, speech processing, and converting text to speech.
This document discusses developing an embedded speech recognition system for mobile devices. It aims to allow users to respond to text messages by voice while continuing other tasks like driving or working. The proposed system would recognize words from voice input and convert text messages to voice messages. It was developed using Visual Basic, Windows Mobile SDK, and a cellular emulator. The system intercepts messages sent to the emulator, stores them in a database, and uses text-to-speech to read the messages aloud. The document reviews related work on speech recognition and text-to-speech conversion techniques. It analyzes the design and tests the system's ability to receive, store and read out messages. Future work could expand the system to enable speech-to-text
This document provides the text of chants and prayers used in the Ethiopian Orthodox Tewahedo Church service. It includes some of the most commonly used chants in Ge'ez, Tigrinya, and English to help congregants participate. The presentation was sponsored by a foundation dedicated to preserving Ethiopian liturgical chants through modern technology and training tools.
This presentation will help you to understand the basic concepts of Natural Language Processing With this you will understand the significance of Natural Language Processing in our daily life
This document provides an outline on natural language processing and machine vision. It begins with an introduction to different levels of natural language analysis, including phonetic, syntactic, semantic, and pragmatic analysis. Phonetic analysis constructs words from phonemes using frequency spectrograms. Syntactic analysis builds a structural description of sentences through parsing. Semantic analysis generates a partial meaning representation from syntax, while pragmatic analysis uses context. The document also introduces machine vision as a technology using optical sensors and cameras for industrial quality control through detection of faults. It operates through sensing images, processing/analyzing images, and various applications.
Stemming And Lemmatization Tutorial | Natural Language Processing (NLP) With ...Edureka!
( **Natural Language Processing Using Python: - https://www.edureka.co/python-natural... ** )
This PPT will provide you with detailed and comprehensive knowledge of the two important aspects of Natural Language Processing ie. Stemming and Lemmatization. It will also provide you with the differences between the two with Demo on each. Following are the topics covered in this PPT:
Introduction to Big Data
What is Text Mining?
What is NLP?
Introduction to Stemming
Introduction to Lemmatization
Applications of Stemming & Lemmatization
Difference between stemming & Lemmatization
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
The document discusses natural language and natural language processing (NLP). It defines natural language as languages used for everyday communication like English, Japanese, and Swahili. NLP is concerned with enabling computers to understand and interpret natural languages. The summary explains that NLP involves morphological, syntactic, semantic, and pragmatic analysis of text to extract meaning and understand context. The goal of NLP is to allow humans to communicate with computers using their own language.
Natural Language Processing(NLP) is a subset Of AI.It is the ability of a computer program to understand human language as it is spoken.
Contents
What Is NLP?
Why NLP?
Levels In NLP
Components Of NLP
Approaches To NLP
Stages In NLP
NLTK
Setting Up NLP Environment
Some Applications Of NLP
Natural Language Processing (NLP) is a subfield of artificial intelligence that aims to help computers understand human language. NLP involves analyzing text at different levels, including morphology, syntax, semantics, discourse, and pragmatics. The goal is to map language to meaning by breaking down sentences into syntactic structures and assigning semantic representations based on context. Key steps include part-of-speech tagging, parsing sentences into trees, resolving references between sentences, and determining intended meaning and appropriate actions. Together, these allow computers to interpret and respond to natural human language.
This document provides an overview of various topics in natural language processing including speech recognition, natural language understanding, natural language generation, chatbots, and machine translation. It discusses key aspects of each topic such as how speech recognition works, the challenges of natural language understanding, and how machine translation systems have evolved to consider context and domain specificity.
Its started off as a part of Artificial intelligence. NLP is challenging , but its been widely researched for future application which will have human touch.
Natural Language Processing: Definition and ApplicationStephen Shellman
Steve Shellman heads Strategic Analysis Enterprises, Inc., an organization that uses academic methodologies and complex techniques such as named-entity extraction and natural language processing to provide innovative solutions for strategic planning. Natural language processing (NLP) began in the 1950s in intelligence and automatic translation and concerns language interactions between computers and humans, allowing computers to understand human speech in real-time, though speech contains ambiguity. Currently, NLP uses machine learning to examine patterns and expand comprehension, being applied to fields like named-entity extraction, deep analytics, and opinion mining.
Natural language processing provides a way in which human interacts with computer / machines by means of voice.
"Google Search by voice is the best example " which makes use of natural language processing..
Our speech to text conversion project aims to help the nearly 20% of people worldwide with disabilities by allowing them to control their computer and share information using only their voice. The system uses acoustic and language models with a speech engine to recognize speech and convert it to text. It can perform operations like opening calculator and wordpad. Speech recognition has applications in areas like cars, healthcare, education and daily life. Accuracy depends on factors like vocabulary size, speaker dependence, and speech type (isolated, continuous). The system aims to improve accessibility while reducing costs.
This document discusses speech synthesis technology. It begins with an introduction defining speech synthesis as the artificial production of human speech. It then discusses the history of speech synthesis, including early inventions and developments of speech synthesizers. It also covers the construction and various approaches to speech synthesis, such as concatenative synthesis and formant synthesis. The document concludes by discussing applications of speech synthesis and remaining challenges.
Past, Present, and Future: Machine Translation & Natural Language Processing ...John Tinsley
This was a presentation given at the European Patent Office's annual Patent Information Conference in Madrid, Spain on November 10th, 2016.
In it, we give an overview of how machine translation works, latest advances in neural MT, and how this can be applied to patents and intellectual property content, not only for translations but also information extraction and other NLP applications.
SCRIPTS AND NUMERALS IDENTIFICATION FROM PRINTED MULTILINGUAL DOCUMENT IMAGEScscpconf
This document presents a technique for identifying scripts (Tamil, English, Hindi) and numerals from multilingual document images using a rule-based classifier. Words are segmented and the first character of each word is represented as a 9-bit vector based on features like density, shape, and transitions. A rule-based classifier containing rules derived from training data is used to classify the script of each character. The technique aims to automatically categorize multilingual documents before applying optical character recognition and requires minimal preprocessing with high accuracy.
Natural Language Processing (NLP) - IntroductionAritra Mukherjee
This presentation provides a beginner-friendly introduction towards Natural Language Processing in a way that arouses interest in the field. I have made the effort to include as many easy to understand examples as possible.
Natural language processing (NLP) analyzes and represents natural language text or speech at linguistic levels to achieve human-like language processing for applications. NLP was influenced by Turing's 1950 paper on machine intelligence and involved early systems like SHRDLU in the 1960s. NLP understands, generates, and integrates natural language through techniques like morphological, syntactic, semantic and discourse analysis to benefit domains like search, translation, sentiment analysis, social media and more.
This document discusses text analysis tools and techniques. It provides an overview of 8 popular text analysis tools, including MonkeyLearn, Aylien, IBM Watson, Thematic, Google Cloud NLP, Amazon Comprehend, MeaningCloud, and Lexalytics. It then describes 5 common text analysis techniques: information extraction, categorization, clustering, visualization, and summarization. Finally, it outlines 7 basic functions of text analytics: language identification, tokenization, sentence breaking, part-of-speech tagging, chunking, syntax parsing, and sentence chaining.
Natural language processing (NLP) is a subfield of artificial intelligence that aims to allow computers to understand human language. NLP involves analyzing and representing text or speech at different linguistic levels for applications like question answering or machine translation. Challenges for NLP include ambiguities in language like lexical, syntactic, semantic, and anaphoric ambiguities. Common NLP tasks include part-of-speech tagging, parsing, named entity recognition, and sentiment analysis. Applications of NLP include text processing, machine translation, speech processing, and converting text to speech.
This document discusses developing an embedded speech recognition system for mobile devices. It aims to allow users to respond to text messages by voice while continuing other tasks like driving or working. The proposed system would recognize words from voice input and convert text messages to voice messages. It was developed using Visual Basic, Windows Mobile SDK, and a cellular emulator. The system intercepts messages sent to the emulator, stores them in a database, and uses text-to-speech to read the messages aloud. The document reviews related work on speech recognition and text-to-speech conversion techniques. It analyzes the design and tests the system's ability to receive, store and read out messages. Future work could expand the system to enable speech-to-text
This document provides the text of chants and prayers used in the Ethiopian Orthodox Tewahedo Church service. It includes some of the most commonly used chants in Ge'ez, Tigrinya, and English to help congregants participate. The presentation was sponsored by a foundation dedicated to preserving Ethiopian liturgical chants through modern technology and training tools.
Projects Completed at the University of ManchesterMike Jones
Manchester Students Union
Installed and upgraded 84 managed desktop PCs to Windows 7, adding additional memory. Work was done after hours to minimize downtime. Supported bespoke systems like ticket and ID card printers. Installed and configured a CCTV security system with remote access. Supported EPOS and accounting software.
Manchester University Press
Moved 25 PCs to a new office, testing network connections and ensuring PCs were working on move day.
Manchester Race Relations
Moved PCs, network switches, phones and upgraded to Windows 7 with a new printer when moving offices.
Campus-wide projects
Managed a 2,500 PC refresh from Windows XP to Windows 7. Trained new staff
The document discusses machine learning and optical character recognition (OCR). It defines machine learning as the study of algorithms that can learn from data without being explicitly programmed. It discusses the types of machine learning algorithms and applications such as spam detection and medical research. The aim of the presentation is to implement OCR using supervised machine learning to convert images of text into machine-encoded text. It describes the OCR process and prospects for using OCR in applications like processing business documents.
The Project is based on design & implementation of smart hybrid system for street sign boards recognition, text and speech conversions through character extraction and symbol matching. The default language use to pronounce signs on the street boards is English. Here we are proposing a novel method to convert identified character or symbol into multiple languages like Hindi, Marathi, Urdu, etc. This Project is helpful to all starting from the visually impaired, the tourists, the illiterates and all the people who travel. The system is accomplished with the speech pronunciation in different languages and to display on screen. This Project has a multidisciplinary approach as it belongs to the domains like computer vision, speech processing, & Google cloud platform. Computer vision is used for character and symbol extraction from sign boards. Speech processing is used for text to speech conversion. GCP is used for multiple language conversion of original extracted text. Further programming is done for real time pronunciation and displaying desired output.
The document outlines a methodology for developing an automatic speech recognition system, including extracting features from speech signals, building acoustic models using tools like hidden Markov models and Gaussian mixture models, and recognizing speech patterns to convert spoken words to text. It discusses challenges in applying speech recognition to under-resourced tribal languages in India and the need to preserve indigenous languages and cultures. The proposed research aims to implement and evaluate speech recognition techniques for tribal languages to help document and promote endangered languages.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
This document discusses font and size identification in Telugu printed documents. It provides background on the Telugu script, which contains a large number of compound characters formed from combinations of vowels and consonants. The document then discusses the need for font and size identification as a preprocessing step for optical character recognition (OCR) systems to improve accuracy. It presents an approach using zonal analysis and connected component analysis to extract features from text images like aspect ratio and pixel ratio to identify the font and size by comparing to a database. Results showed this approach could accurately identify different fonts and sizes in Telugu text images.
This document discusses computational linguistics, including its origins, main application areas, and approaches. Computational linguistics originated from efforts in the 1950s to use computers for machine translation between languages. It aims to develop natural language processing applications like machine translation, speech recognition, and grammar checking. Research employs various approaches including developmental, structural, production-focused, and comprehension-focused methods. The field involves both computer science and linguistics.
DICTIONARY BASED AMHARIC-ARABIC CROSS LANGUAGE INFORMATION RETRIEVALcsandit
The demand for multilingual information is becoming erceptive as the users of the internet throughout the world are escalating and it creates a problem of retrieving documents in one language by specifying query in another language. This increasing demand can be addressed by designing automatic tools, which accepts the query in one language and retrieves the relevant documents in other languages. We have developed prototype Amharic-Arabic Cross Language
Information Retrieval System by applying dictionary-based approach that enables the users to retrieve relevant documents from Amharic-Arabic corpus by entering the query in Amharic and retrieving the relevant documents both Amharic and Arabic.
This document provides an introduction to character recognition and optical character recognition (OCR). It discusses the purpose and history of OCR, including early technologies from the 1910s-1930s. It also covers the scope, technology used, and how to use OCR software. Finally, it discusses the feasibility study for an OCR project, including technical, operational, and economic feasibility. The overall purpose is to develop an efficient OCR software system to convert paper documents to electronic format for improved document processing and searchability.
IRJET - Language Linguist using Image Processing on Intelligent Transport Sys...IRJET Journal
This document summarizes a research paper that proposes a system to detect text in images of traffic signs, extract the text, and translate it to English. The system uses convolutional neural networks (CNNs) to detect text areas and recurrent neural networks (RNNs) to translate the extracted text. The goal is to help travelers understand traffic signs written in unfamiliar languages like Spanish or French by automatically translating the text in images to English. The system performs three steps: 1) detect text areas in images of signs, 2) extract the words from the detected text regions, and 3) translate the extracted text to English for the user.
LIT (Lexicon of the Italian Television) is a project conceived by the Accademia della Crusca, the leading research institution on the Italian language, in collaboration with CLIEO (Center for theoretical and historical Linguistics: Italian, European and Oriental languages), with the aim of studying frequencies of the Italian lexicon used in television content and targets the specific sector of web applications for linguistic research. The corpus of transcriptions is constituted approximately by 170 hours of random television recordings transmitted by the national broadcaster RAI (Italian Radio Television) during the year 2006.
This document discusses offline handwritten Devanagari script recognition using a probabilistic neural network. It begins with an abstract that outlines the goal of recognizing offline handwritten Devanagari numerals using structural and local features classified with a probabilistic neural network classifier. The introduction provides background on handwritten numeral recognition challenges. The document then reviews related work on character recognition from the early 1900s to modern advancements, describes the Devanagari script, discusses theoretical neural network and proposed recognition methods, and concludes that accurate recognition depends on the input quality and more efficient, accurate systems are needed to recognize varied writing styles.
The document discusses the field of computational linguistics, defining it as the scientific study of language from a computational perspective. It involves providing computational models of linguistic phenomena and using computational techniques and linguistic theories to solve problems in natural language processing. Computational linguistics aims to automatically process and understand natural language by constructing computer programs. The field has its roots in the 1940s-1950s with the development of code breaking machines and computers. Major conferences and journals in the field are associated with the Association for Computational Linguistics.
How to build language technology resources for the next 100 yearsGuy De Pauw
The document discusses how to build sustainable language technology resources for lesser-resourced languages over the next 100 years. It outlines an vision of linguistic diversity and language survival. Key challenges include limited resources, small language communities, and technological limitations. Approaches proposed to work around these include minimizing redundant work, maximizing reuse of resources, building user and developer communities, and preparing resources to work with future technologies. Specific topics covered are types of language technology resources, issues around character encoding, text input methods, and future-proofing keyboard layouts and recognition technologies for many languages.
Summer Research Project (Anusaaraka) ReportAnwar Jameel
This document discusses Anusaaraka, a machine translation tool being developed to translate between English and Hindi. It uses principles from Panini's grammar to map word groups and constructions between the languages. Where differences exist, extra notation is added to preserve source language information. The output is presented in layers to show the translation process. It aims to bridge the language barrier by allowing users to access text in their preferred Indian language.
Key features include faithfully representing the source text, reversibility of the translation process through layered output, and transparency by allowing users to trace the translation steps. It was developed by combining traditional Indian linguistic principles with modern technologies.
Design and Development of Morphological Analyzer for Tigrigna Verbs using Hyb...kevig
Morphological analyzer is the basic for various high level NLP applications such as information retrieval, spell checking, grammar checking, machine translation, speech recognition, POS tagging and automatic sentence construction. This paper is carefully designed for design and analysis of morphological analyzer Tigrigna verbs using hybrid of memory learning and rules based approaches. The experiment have conducted using python 3 where TiMBL algorithms IB2 and TRIBL2, and Finite State Transducer rules are used. The performance of the system has been evaluated using 10 fold cross validation technique. Testing conducted using optimized parameter settings for regular verbs and linguistic rules of the Tigrigna language allomorph and phonology for the irregular verbs. The accuracy on the memory based approach with optimized parameters of TiMBL algorithm IB2 and TRIBL2 was 93.24% and 92.31%, respectively. Finally, the hybrid approach had the actual performance of 95.6% using linguistic rules for handling irregular and copula verbs.
DESIGN AND DEVELOPMENT OF MORPHOLOGICAL ANALYZER FOR TIGRIGNA VERBS USING HYB...kevig
Morphological analyzer is the base for various high-level NLP applications such as information retrieval,
spell checking, grammar checking, machine translation, speech recognition, POS tagging and automatic
sentence construction. This paper is carefully designed for design and analysis of morphological analyzer
Tigrigna verbs using hybrid of memory learning and rules based approaches. The experiment have
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Optical character recognition for Ge'ez characters
1. 0Introduction
Mekelle Institute of Technology
Project proposal
For
2016
Project Members Dep’t
1. Awet Haileslassie CSE
2. Hadush Hailu CSE
3. Mulu Hailemariam CSE
4. Negash Desalegn CSE
Submission date: March, 28 2016
OCR Algorithm for Ge'ez
Characters
2. OCR Algorithm for Ge'ezCharacters
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Abstract
It won't be an exaggeration to claim that Ethiopia's intellectual property is hardly
digitized; and is stored in paper be it in the form of century old parchment paper in
monasteries or in the form of file cabinets in various regional and federal offices.
Digitizing the plethora of documents by hand is not a feasible affair. To this end,
building a system that identifies and digitizes documents made up of Ethiopian
characters is a logical move. This system would play pivotal role in preserving the
intellectual propertyof the country and making it easily accessible for the posterity.
3. OCR Algorithm for Ge'ezCharacters
P a g e 2 | 10
Contents
Introduction......................................................................................................................................3
Background......................................................................................................................................4
Problem Statement...........................................................................................................................5
Proposed System...............................................................................................................................6
Objective...........................................................................................................................................7
General Objective .........................................................................................................................7
Specific Object..............................................................................................................................7
Limitation.........................................................................................................................................8
Methodology.....................................................................................................................................8
1. The Imaging Stage.....................................................................................................................8
2. The OCR Process ......................................................................................................................9
3. Distinguishing between text and images - Segmentation............................................................9
4. Character recognition - Feature Extraction...............................................................................9
5. Recognition of Words................................................................................................................9
6. Correction of Unrecognized Characters – Error Correction.....................................................9
7. Output Formatting....................................................................................................................9
Projectschedule plan......................................................................................................................10
References.......................................................................................................................................10
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Introduction
Optical Character Recognition (OCR) is a technology that is used to translate
scanned images of text into computer editable and searchable text. OCR Software
are analytical artificial intelligence systemthat consideronly sequences ofcharacters
rather than whole words or phrases and do not cross-validate data during the
recognition process based on the analysis of sequential lines and curves, OCR make
‘best guesses’ at characters using database look-up tables to closely associate or
match the strings of characters that form words. For this system to effectively
recognize hand printed or machine printed forms, words must be separated into
individual characters. That is why most typical administrative forms require people
to either hand print into neatly spaced boxes oruse combs(tickmarks) at the bottom
of input lines to force spaces between letters entered on a form. without the use of
combs or boxes, conventional technologies reject fields if people do not follow the
structure when filling out forms, resulting in significant administrative overhead and
costs to forms processing organizations. (OCRopus, 2009)
OCR is a process that allows printed (typewritten, printout as well as handwritten)
text to be recognized optically and converted into machine–readable code that
can be accepted by a computer for further processing (Genovese, 1970). OCR
systems provide a tremendous opportunity in handling repetitive, boring, labor-
intensive, error prone, and time consuming processes for human beings.
Among others, the following are the major advantages of the OCR technology:
• It can be used to scan and preserve historical documents.
• It can be used for scanning data entry forms in a faster and less error prone manner.
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At present the recognition of Latin-based characters from well-conditioned
documents can be considered as a relatively feasible technology. On the other hand,
the processing of non-Latin scripts is still a subject of active research.
Ethiopic script-based OCR processing is currently among the least developed ICT
disciplines in the country. Developments in this area are mainly limited to
preliminary research activities undertaken at different institutions of higher
educations, such as the former School of Information Science for Africa (SISA).
Such efforts are undertaken in an uncoordinated ways.
Background
Ge'ez (ግዕዝ also referred to by some as "Ethiopic") is an ancient South Semitic
language that originated in the northern region of Ethiopia and Eritrea in the Horn
of Africa. It later became the official language of the Kingdom of Aksum and
Ethiopian imperial court.
Today, Ge'ez remains only as the main language used in the liturgy of the Ethiopian
Orthodox Tewahedo Church, the Eritrean Orthodox Tewahedo Church, the
Ethiopian Catholic Church, and the Beta Israel Jewish community. However, in
Ethiopia Amharic (the main lingua franca of modern Ethiopia) or other local
languages, and in Eritrea and Tigray Region in Ethiopia, Tigrigna may be used for
sermons. Tigrigna and Tigre are closely related to Ge'ez with at least four different
configurations proposed.[1]
Some linguists do not believe that Ge'ez constitutes the
common ancestor of modern Ethiopian languages, but that Ge'ez became a separate
language early on from some hypothetical, completely unattested language,[2]
and
can thus be seen as an extinct sister language of Tigre and Tigrinya.[3]
The foremost
6. OCR Algorithm for Ge'ezCharacters
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Ethiopian experts such as Amsalu Aklilu point to the vast proportion of inherited
nouns that are unchanged, and even spelled identically in both Ge'ez and Amharic
(and to a lesser degree, Tigrinya).[4]
Amharic is a Semitic language spoken in Ethiopia. It is the second-most spoken
Semitic language in the world, after Arabic, and the official working language ofthe
Federal Democratic Republic of Ethiopia. Amharic is also the official or working
language of several of the states within the federal system.
It has been the working language of government, the military, and the Ethiopian
Orthodox Tewahedo Church throughout medieval and modern times. The 2007
census counted nearly 22 million native speakers in Ethiopia.[5]
Outside Ethiopia,
Amharic is the language of some 2.7 million emigrants. It is written (left-to-right)
using Amharic Fidel, ፊደል, which grew out of the Ge'ez abugida—called,
in Ethiopian Semitic languages, ፊደል fidel ("writing system", "letter", or
"character") and አቡጊዳ abugida (from the first four Ethiopic letters, which gave rise
to the modern linguistic term abugida).[6]
Problem Statement
What should you do if you want to convert scanned paper, books and documents
into electronic files like Word document, PDF, or text? You need Optical character
recognition, usually abbreviated to OCR, which can translate scanned images of
handwritten, typewritten or printed text into machine-encoded text.
The motivation that the fact for initiating this project is the absences of locally and
or internationally developed single production for optical character recognition
software for Geez characters. The language is not supported by ASCII standard to
7. OCR Algorithm for Ge'ezCharacters
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use it onthe computer. All these fonts have some similar characters and some others
are difficult to swap from one font to another font. This shows no standardized font
is found in the country. Due to lack of this standard font, the country lacks different
types of services and applications like OCR.
Proposed System
Advanced OCR algorithm that uses both openCV(open Computer vision) and
ANN(Artificial Neural Network) that allows the conversion of scanned images of
handwritten, typewritten, or printed text into machine-encoded text.
Open source OCR software called Tesseract Engine will be used as a basis for
Optical Recognition project into text or information that can be understood oredited
using computers, which is considered as the most accurate free OCR engine in
existence.
8. OCR Algorithm for Ge'ezCharacters
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Objective
In Ethiopia there is no any single quality OCR application for Ge'ez characters. In
general OCR technologies are a well solved problem and matured technology for
Latin scripts, but for non-Latin scripts like some Asian and African scripts still it is
a non-solved problem and active research area. Ge'ez characters are non-Latin
scripts. And relatively with Latin scripts the Ge'ez characters are about 380 and 310
are most popularly and frequently used.
General Objective
Building a system that identifies and digitizes documents made up of Ethiopian
characters is a logical move. This system would play pivotal role in preserving the
intellectual propertyof the country and making it easily accessible for the posterity.
Specific Object
The project would entail many tasks including:
1. Machine learning: to train the system to recognize Ethiopic fonts (Doing
This with high fidelity especially for century old hand written documents would be
an interesting challenge).
2. Parallelizing the developed OCR algorithm to minimize the amount of time it
takes to do the task.
9. OCR Algorithm for Ge'ezCharacters
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3. Once the document is scanned, the words on the document need to be compared
against the list of all known Geez, Amharic and Tigrigna . . . . Words for error
identification/correction.
Limitation
Ethiopic language has its own symbols and syllables for numbers, and mostly for
writing purpose the language shares the Arabic numeric styles such as
0,1,2,3,4,5,6,7,8,9. Due to this reason the Ethiopian numeric styles are not included
in this project.
Methodology
The process of converting Ge'ez character images into electronic form which is
usually referred to as digitization is undertaken in different steps.
The process of scanning a document and representing the scanned image for further
processing is called the preprocessing or imaging phase.
The process of manipulating the scanned image of a document to produce a
searchable text is called the OCR processing stage.
1. The Imaging Stage
The imaging process involves scanning the documentand storing it as an image. The
mostpopular image format used forthis purposeis called Tagged-Image File Format
(TIFF).
10. OCR Algorithm for Ge'ezCharacters
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The resolution (number of dots per inch – dpi) determines the accuracy rate of the
OCR process.
2. The OCR Process
The major steps of the OCR processing stage are shown below.
3. Distinguishing between text and images - Segmentation
In this step, the process ofidentifying the text and image blocks ofthe scanned image
is undertaken. The boundaries of each image are analyzed in order to recognize the
text.
4. Character recognition - Feature Extraction
This step involves recognizing a character using a method known as feature
extraction. OCR tools store rules about the characters of a given script using a
method known as the learning process. A character is then identified by analyzing
its shape and comparing its features against a set of rules stored on the OCR engine
that distinguishes each character.
5. Recognition of Words
Following the character recognition process, word identification process is
performed by comparing string of characters against an existing Ge'ez dictionary
words.
6. Correction of Unrecognized Characters – Error Correction
In this step, the user is allowed to provide corrections to unrecognized characters.
7. Output Formatting
The final step involves storing the output in one of the industry standard formats
such as PDF, WORD and plain UNICODE text.
11. OCR Algorithm for Ge'ezCharacters
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Project schedule plan
ProjectAction Required Time
Collection of data One week
Collection of system requirements One week
Methodology Two weeks
System design and implementation 1
1
2
month
Results and observation One week
References
[1] Bulakh, Maria; Kogan, Leonid (2010). "The Genealogical Position of Tigre and
the Problem of North Ethio-Semitic Unity". Zeitschriften der Deutschen
Morgenländischen Gesellschaft 160 (2): 273–302.
[2] Connell, Dan; Killion, Tom (2010). Historical Dictionary of Eritrea (2nd,
illustrated ed.). Scarecrow Press. p. 508. ISBN 978-0-8108-7505-0.
[3] Haarmann, Harald (2002). Lexikon der untergegangenen Sprachen [Lexicon of
extinct languages] (in German) (2nd ed.). C. H. Beck. p. 76. ISBN 978-3-406-
47596-2.
[4] Amsalu Aklilu, Kuraz Publishing Agency, ጥሩ የአማርኛ ድርሰት እንዴት ያለ ነው!p. 42
[5] http://catalog.ihsn.org/index.php/catalog/3583/download/50086
[6] Amharic Wikipedia url -> https://en.wikipedia.org/wiki/Amharic