How does Google Lens use this algorithm for translations?

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Let's imagine that someone took a photo of a recipe in Italian, we really want to make it, but we don't know the language. By analyzing the photo in Google Lens, we are able to translate it.

Text Extraction and Topic classification are two applications that go hand in hand. Both of these applications focus on context analysis.

Text extraction is a tool that allows you to highlight important information from a large amount of data. The algorithm analyzes a certain text and highlights the most important things in it depending on the key imposed on it; these can be, for example, colors, places (locations) or special features.

The algorithm that deals with topic classification analyzes the prepared text and assigns a specific topic to the fragments. As
in the case of sentiment analysis, the algorithm must learn how to assign these topics, so a certain key is defined that will facilitate learning.

How can businesses use text analysis to analyze topics or elements in the text?
For example, a company that manufactures dresses, for cameroon whatsapp lead example, can "catch" what customers are missing by reading opinions about the product (e.g. customers are missing short white dresses because the holiday season is approaching and they would like to have them) or decide to make changes based on opinions. When a customer describes that the delivery time is too long, we can categorize this particular opinion as "delivery". If there are more and more opinions categorized as delivery, the entrepreneur can react and improve their services.

Chatbots are applications mainly used for customer service. Such a chatbot can be implemented on the website of a company, store, etc., where it will be used to contact
customers, answer questions and solve problems. Such a chatbot is available 24/7, 365 days a year, so using a chatbot we can give up the entire customer service department, saving money and gaining time.

The possibilities are endless, the only limit is our imagination.

FAQ
What is Natural Language Processing?
Natural Language Processing (NLP) is an interdisciplinary field of computer science that focuses on modeling natural language and applying algorithms to understand text or speech. The main goal of NLP is to transform natural language so that computers can easily use it to perform tasks without human intervention. NLP has applications in many different industries, from intelligent assistants to machine translation systems.

What is the meaning of natural language?
Natural language is important because it is the primary way humans communicate with each other. Since there are so many different languages, NLP can help computers understand different languages ​​and enable them to communicate better with humans.

What are the most popular components of NLP?
The most popular components of NLP are sentence analysis, speech recognition, lemmatization, tokenization, and algorithm training. Sentence analysis involves analyzing the grammatical structure of a sentence to determine its function and semantics. Speech recognition is used to convert speech into text, and lemmatization is used to inflect words according to their natural language inflections. Tokenization, on the other hand, is the process of breaking text into small pieces to make it processable. Algorithm training involves machine learning behavior models.