Latin Dictionary helps scholars and students find meanings and definitions they need, quickly, accurately, and without hassle.
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This is a web platform offering a powerful and accessible online Latin dictionary. It is designed for researchers, scholars, and students to get precise definitions and context using advanced filtering. Users can now work with Latin texts more efficiently by combining reliable data and easy-to-use tools.
The client approached us to improve their existing dictionary. They needed to add advanced features for a global academic audience. Their key requirements were:
We help scholars and students find what they need quickly, accurately, and without hassle
Accurate Search Results
Under 1 Second Search Time
Faster Content Access
Provides four main filters: Lemma (root words), Full Text (highlights within full entries), Citations (filters by author/source), and Index Librorum (sorts by age, edition, and notes).
Extra filters for volume, pages, and lines are available. Also, it supports multiple row queries and Boolean conditions.
Users can browse entries by initial letters across Lemma, Author Cited, and Index Librorum.
Accepts characters like ‘*’, ‘?’, or ‘!’ to replace letters or parts of words.
You can search exact matches of terms that are placed inside quotation marks.
The system supports wildcards and synonyms.
Our main aim was to accurately analyse thousands of Latin words. Many of them included Greek characters and special symbols.
Since it was handling complex character encoding and large volumes of data, fast search results were a challenge.
Creating a user interface that was both responsive and intuitive for scholars required careful design choices.
The search engine had to recognize interchangeable characters, such as I and O or U and V, without affecting accuracy.
Supporting advanced and multi-condition queries posed a performance challenge that had to be addressed without slowing down results.
Our team redesigned Latin Dictionary's architecture to handle both performance and precision. We integrated AWS Elastic Search for near-instant querying. Also, we used MongoDB to manage extensive lexical data. Spring Boot on the backend enabled scalable processing, and ReactJS provided a fluid and responsive interface.
The system was built to ensure ease of use for scholars. There are many features like flexible filtering, character recognition, multi-condition search, and alphabetical browsing that make the tool reliable and fast for academic work.
We use reliable and cutting-edge technologies to create secure, efficient, and scalable solutions that turn your ideas into powerful products.
React
Spring Boot
AWS EC2
AWS Elastic Search
Mongo DB
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