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Databases for Tamil Languages: Development and Utilization

S. Sweetlin Devamanohari M.Sc., M.Phil, (Ph.D) & P. Karpagaselvi., M.C.A

Assistant Professor, Department of Computer Science

GTN Arts College, Dindigul.

1263

Summary

This article explores the development, challenges and use of Tamil repositories ad highlights their important role in the preservation of one of the longest surviving classical languages in the world. The Tamil language has a long historical and cultural significance and careful efforts should be made to create and maintain a repository for various purposes. These archives preserves heritage and support modern languages, cultures and studies. It delves in the architecture of these storage facilities and highlights the methods and techniques used to ensure their robustness, scalability and availability This article also discusses various challenges faced including specifying Tamil text and grammar, building data model, ensuring data security and privacy etc… They also provide important resources for language research and teaching. Technically these storage facilities are important to promote Tamil heritage in cultural studies and develop language processing tools. This article demonstrates impact and potential of effective Tamil repositories.

Key Words: Repositories, Heritage, Research, Tamil, Culture, Archives.

Introduction:
Tamil is one of the oldest languages and has a rich literature spanning over two thousand years. This includes literature, epics, spirituality and modern literacy creations. In the digital age, creation of repositories is essential to preserve a vast body of knowledge and make it available for a variety of applications. A comprehensive review of various Tamil databases has been conducted, including lexical databases (that has words and their meanings), multimedia databases (audio, video recordings). It also explores the methods and technologies used in the development of those such as optical character recognition (OCR) to digitize printed documents, natural language processing (NLP) for the analysis of language models and solutions that can be stored in cloud computing. 

Importance of Tamil Databases:
The use of Tamil language repositories in the current context is diverse. In academic field it allows researchers to make detailed observations of language use. In education they enhance the classroom through interactive and integrated information by providing digital tools for language learning. In terms of cultural preservation, the Tamil repository helps preserve and promote the rich heritage and makes it available to the global audience. On the technology side, they are supporting the development of text-to-text language applications such as machine translation, speech recognition and sentiment analysis to facilitate communication and information exchange in Tamil.

Types of Tamil Databases:
  • Literary Databases
    • The archive contains digitized copies of Tamil literature, from Sangam poetry to novels and academic publications. They help scholars conduct research, conduct comparative studies and preserve rare manuscripts. By providing high-tech copies of these works, archives also support the preservation of cultural heritage and it transfers to future generations. They also provide powerful narrative, interactive tools that are deeper interactive research. This approach makes Tamil literature more accessible and encourage interest on rich writing.

Examples of Literary Databases:
 Project Madurai: An open-source initiative that digitizes ancient Tamil literary works and makes them available for free.
 Sangam Project: Focuses on the digital preservation and dissemination of Sangam literature.
 Tamil Virtual Academy: Offers a range of digital resources including e-books, articles, and research papers on Tamil literature.
  • Lexical Databases:
    • Lexical databases are comprehensive collection of words in a language and their meanings, uses, relationships etc… They contain detailed information about the words used in a language. The archives often contain about the origin etymology of a word and shed light on historical development of the language. Data points facilitate language research by organizing words into structures and perform tasks such as machine translation, natural language processing (NLP) and text Analysis. They are important tools for lexicographers, linguists, educators and software developers to understand better and use the language. In addition language libraries support the development of language learning applications and tools by understanding subtle meanings. Through the integration of computing systems these documents can be updated and expanded to reflect changes in the language and include new words as they emerge.


  • Historical and Cultural Databases:
    • Historical documents, inscriptions and cultural artifacts are stored in these archives. Preserving and studying heritage is important for historians, archaeologists and cultural scientists[3]. By digitizing and cataloguing these important resources, archives ensure that rare and sensitive items are protected from physical deterioration and remain available for research. They are useful to examine ancient text and artifacts at high resolution that reveals details that traditional methods cannot reveal. Expanding meta-data with each entry helps place the item in context, providing an understanding of the items history, meaning and background. These resources are freely available online, allowing students, teachers and enthusiasts worldwide to explore Tamil heritage without the constraints of geography and transportation.


Preservation Initiatives:
Organization such as Tamil Virtual Academy and various academic institutions have taken significant steps in digitizing historical documents and cultural artifacts. These efforts ensure that invaluable historical resources are preserved and made available to researchers all around the world.

  • Spoken Tamil Databases:
    • These databases contains Tamil language recordings in a variety of languages and context. They are important for phonetic studies, allowing researchers to observe subtle differences in speech and intonation requires to train the system to speak accurate and effective Tamil. Moreover language studies benefit greatly from this archives as they provide rich resources to study the evolution, diversity and structure of Tamil language.
  • Dialectal Diversity
    • Given Tamil's wide geographical spread, spoken Tamil databases capture the linguistic diversity of the language. Projects like the Spoken Tamil Corpus provide valuable data for phonetic analysis and speech technology development.

Database Architecture:
  • Data Collection;
    • Collecting data for the Tamil database is a complex and labour-intensive task required to create a powerful and comprehensive language databases. Collection of various audio recordings, documents and manuscripts from different regions, covering the entire spectrum of Tamil language and communities are involved. Fieldwork is often necessary as researchers travel to remote and urban areas to record speakers from a variety of background, from regular conversation to formal speeches. Good recording equipment and clear instructions are used to maintain data integrity and accuracy. Also the process includes working with local communities and linguists to ensure cultural accuracy. This system preserves the heritage of the language, making it useful for phonetic research, language counting and the development of advanced speech recognition[10].
    • This process requires collaboration with libraries, cultural institutions, and the Tamil-speaking community. Collaborative efforts between academic institutions, cultural organizations, and volunteers are essential for comprehensive data collection. Community-driven projects often lead to more extensive and culturally relevant databases.
  • Data Storage
    • Latest technology is used in maintaining the Tamil database to ensure effective management, easy access and storage of information in multiple languages. Todays solutions use cloud-based platforms such as AWS and Google cloud to provide flexibility and resilience to host continuous data from multiple sources. Formats like XML, JSON, and SQL are commonly used for structuring data.
    • Data compression algorithms are used to improve storage without compromising recording and transfer quality. Redundancy and back-up strategies are carefully implemented to prevent data loss and ensure reliability of data. Encryption and access control systems are integrated to protect language-related information from unauthorized access.
    • Real-time data collection provides instant access to new information needed for dynamic research and application development.
  • Data Retrieval
    • Advanced indexing techniques such as inverted indexes and n-gram models, help speed-up retrieval and querying. Recent search algorithms such as neural-network based semantic search, allow context-aware queries, allowing users to find relevant information even without using exact terms. Natural Language processing (NLP) technology enhances research by understanding and processing Tamil questions and converting them int different languages and grammars. Use of machine learning models continues to improve research by learning user interactions and query patterns. Technologies like Elasticsearch and Apache Solr are often used to enhance search capabilities.
    • Implementing advanced search functionalities, including full-text search, faceted search, and filtering, improves the usability of Tamil databases. These technologies make it easier for users to find specific information efficiently.

Technologies Used:
  • Optical Character Recognition (OCR);
    • Advances in OCR technology have increased the accuracy and efficiency of digitizing Tamil printed texts. Modern OCR techniques include deep learning and neural network algorithms on complex documents to ensure excellent readability. OCR tools designed for Tamil text such as those created by Gnana Prasath, provide special functions to render Tamil text well. Tools such as Tesseract OCR have been adapted for Tamil, enabling the digitization of vast amounts of printed material. Integration of advanced text search standards such as CRAFT and PARSEQ, enhance the ability to recognize and digitize Tamil texts from variety of sources, that includes written and signed documents. It is now integrated to regular apps like Snipping tool in windows 11 and support Tamil text recognition. This integration makes it easy to digitize and edit Tamil texts directly from the desktop.
  • Natural Language Processing (NLP)
    • NLP is playing a important role in the development and exploration of Tamil literature, pioneering many new applications. This includes tokenization, part-of-speech tagging, named entity recognition, and sentiment analysis. Machine translation is well powered by NLP to provide accurate translation of Tamil and other languages. NLP algorithms analyse social media posts, comments and other user-generated contents to gauge public opinion on various topics[2]. This is particularly important for business and policy makers to understand the social attitudes and trends of Tamil speakers.
    • NLP applications for Tamil include machine translation, text summarization, and sentiment analysis. These applications enhance the usability of Tamil databases in various domains, from academic research to customer service.

  • Machine Learning
    • Machine learning models are used to develop predictive text, automatic translation, and other intelligent applications. These models require large datasets and computational resources for training. Using machine learning algorithms information can be learned from multilingual data, recognizes patterns, predicts outcomes and make decisions[5]. For example, Supervised learning algorithms are trained on Tamil text files to perform tasks such as text classification, speech recognition and translation with high accuracy. It facilitates development of applications using deep learning models and recurrent neural networks (RNN), virtual assistants and automated relay service to process and interpret Tamil language. It accurately captures the nuances of pronunciations and intonation across different languages and contexts. Convolution neural network (CNN) and Transformer models are employed for text recognition. Its important to digitize historical documents. Reinforcement learning another branch of machine learning used to improve language models for a specific task. The combined learning in IoT improves the functionality and also supports the development of new applications such as predictive text input, automatic translation and smart search.

  • Speech Recognition:
    • Speech recognition technology is used to transcribe spoken Tamil into text. This is particularly useful for creating spoken language databases and developing voice-activated applications. Using the state-of-art algorithms speech can be captured and recognized across the languages. This technology is good at capturing nuances, which is crucial for creating a clear and accurate speech library [10]. Tamil materials will be updated regularly and expanded through real-time audio commentary that uses modern language and slang. It results in the development of applications such as virtual assistance, consumer bots and language learning tools for native Tamil Speakers.
    • Speech recognition systems like Google's Speech-to-Text and CMU Sphinx have been adapted for Tamil, facilitating the development of voice-activated services and spoken language research.

Applications:
  • Academic Research:
    • Research initiatives leveraging Tamil databases include linguistic studies on language evolution, comparative literature analyses, and digital humanities projects. These databases provide essential resources for scholarly inquiry. Dialect studies include the examination of regional variations.
  • Education:
    • Educational tools, including language learning apps and digital textbooks, rely on Tamil databases to provide accurate and comprehensive content to learners.
    • E-learning platforms enhance the creation and interaction of lessons among students at different levels [8]. Digital learning tools such as Duolingo and Quizlet incorporate Tamil databases to offer interactive language learning experiences. These tools cater to both native speakers and learners of Tamil as a second language.
  • Technology Development;
    • Tamil databases support the development of various technologies, including search engines, translation tools (machine translation), and virtual assistants, enhancing their ability to understand and process Tamil. Chatbots are conversational agents that understands and responds. Advanced business application provide multilingual customer service and support in Tamil [5].
    • Innovations in technology, driven by Tamil databases, include the development of smart assistants (speech recognition) like Siri and Google Assistant that can understand and respond in Tamil. These advancements broaden the accessibility and usability of technology for Tamil speakers. Applications are developed to convert speech-to-text and text-to-speech.
  • Cultural Preservation and information retrieval;
    • By digitizing and storing Tamil cultural artifacts and literary works, databases play a crucial role in preserving the Tamil heritage for future generations. Large texts are summarized using content summarization [3]. Enhanced algorithms for information retrieval helps to quick access of data.
    • Preservation projects like the Tamil Heritage Foundation work towards digitizing and archiving Tamil manuscripts, palm-leaf manuscripts, and other cultural artifacts [7]. These efforts ensure the longevity and accessibility of Tamil heritage.
  • Governance and Entertainment:
    • E-governance implement systems for administrative services and public interactions in Tamil. Market research and analysis can be made to study the customer behaviour in Tamil speaking regions.
    • Regarding media, content creation supports the development of Tamil content movies, TV shows and online media. With content recognition subtitling and dubbing is also possible.

Challenges;
There are many challenges in creating and maintaining Tamil Databases. A major problem is the complexity of the Tamil text, which consists of many characters and letters, making accurate representation and understanding difficult. Moreover, the lack of digitized Tamil hinders the creation of comprehensive archives. Differences in languages and regional usage make modelling difficult. Providing the best information and avoiding errors require a lot of manual work and skill. Lack of technical skills in Tamil also restricts the use. Maintaining these repositories involve regular updates and quality checks to accommodate evolving languages and new content[1]. It also requires regular investment in resources and technology. Limited resources including financial, technical and human resources are included in poor maintenance especially in non-commercial projects.
 
Securing funding from government grants, private donors, and international organizations is crucial. Partnerships with technology companies and academic institutions can provide the necessary resources and expertise. Lack of standardization in encoding and metadata formats can lead to compatibility issues, making it difficult to integrate different databases and tools.
Data Quality
Ensuring the accuracy and completeness of data is a major challenge. OCR errors, incomplete metadata, and inconsistencies in data can impact the reliability of the database[1]. Implementing stringent quality control measures, including manual verification and validation processes, helps in maintaining the integrity of the data. Crowdsourcing corrections can also improve data quality.
Developing and adopting standardized encoding formats and metadata schemas ensures compatibility and interoperability between different databases and applications [9]. Organizations like the Unicode Consortium play a critical role in this effort.

Future Directions:
Collaborative Efforts
Collaboration between academic institutions, government bodies, and private organizations can pool resources and expertise, accelerating the development of Tamil databases [6]. Engaging in global collaborations with institutions like the Digital Public Library of America and UNESCO can enhance the scope and impact of Tamil databases. International partnerships bring diverse perspectives and resources to the table.

Technological Innovations
Investing in cutting-edge technologies, such as deep learning and cloud computing, can enhance the capabilities of Tamil databases, making them more efficient and user-friendly. Continued research and development in areas like deep learning, big data analytics, and cloud infrastructure will drive the evolution of Tamil databases [6]. These innovations will lead to more robust and scalable solutions.

Community Involvement:
Engaging the Tamil-speaking community in data collection, validation, and annotation processes can improve the quality and coverage of databases. Crowdsourcing initiatives, where community members contribute to data collection and validation, can significantly enhance the richness and accuracy of Tamil databases. Platforms like Wikipedia and Google Maps have successfully utilized this model.

Open Access:
Promoting open access to Tamil databases ensures that researchers, educators, and the public can benefit from the wealth of information, fostering greater usage and innovation. Implementing open data policies and providing API access to Tamil databases encourage wider use and integration into various applications [6]. Open access also promotes transparency and collaborative innovation.

Conclusion:
The development of Tamil databases is a vital endeavour for preserving the linguistic and cultural heritage of the Tamil language. Despite the challenges, the progress in technology and collaborative efforts promise a future where Tamil databases can serve as a rich resource for research, education, and technological development. Continued investment and innovation in this field will ensure that the Tamil language and its cultural treasures are preserved and accessible for generations to come.

References:
  1. Anandakumar, K., & Somasundaram, K. (2021). Digitizing Tamil Literature: Challenges and Solutions. Journal of South Asian Studies.
  2. Raman, V., & Priya, R. (2019). Applications of Natural Language Processing in Tamil. International Conference on Computational Linguistics.
  3. Selvan, T., & Kumar, A. (2020). Preserving Tamil Heritage through Digital Databases. Proceedings of the Digital Humanities Conference.
  4. M. Rajendran, S. Swaminathan, and K. Ganesan (2018). Building a Comprehensive Tamil Corpus for Linguistic Research. Journal of Language Resources and Evaluation.
  5. K. Saravanan, R. Baskaran, and M. Rajendran (2019). Challenges in Developing Multilingual Databases: A Case Study of Tamil. International Journal of Information Management.
  6. T. K. Priya, N. Malarkodi, and R. A. Kumar (2020). Applications of Natural Language Processing in Tamil: Current Trends and Future Directions. Journal of Artificial Intelligence Research.
  7. S. Lakshmanan, R. Venkatesan, and P. Aravindan (2021). Digitizing Tamil Manuscripts: Preservation and Access. Digital Humanities Quarterly.
  8. R. Sundar, V. Meena, and K. Subramanian (2022). Enhancing E-Learning Platforms with Tamil Databases. International Journal of Educational Technology.
  9. N. Ramesh, S. Kavitha, and M. Balasubramanian (2019). Standardizing Tamil Text Encoding for Digital Libraries. Library and Information Science Research.
  10. P. Rajasekar, R. Vidhya, and T. Murugan (2021). Speech Recognition and Synthesis Systems for Tamil. IEEE Transactions on Audio, Speech, and Language Processing.
Author
கட்டுரையாளர்

S. Sweetlin Devamanohari M.Sc., M.Phil, (Ph.D) & P. Karpagaselvi., M.C.A

Assistant Professor, Department of Computer Science

GTN Arts College, Dindigul.