Geek Ideas

Geek idea 1:

Automated Article Content Summarization & Topic Identification System

Overview: The idea here is to create an NLP-based tool that reads a published article and automatically identifies the key topics, themes, and a succinct summary of what the article is about based on its content, title, keywords or tags. It could handle academic papers, news articles, or even blog posts. To achieve this, we might use the following python features: Topic Modeling, Text Summarization, and Named Entity Recognition (NER). The system would be able to extract key topics and provide a concise summary of the article to give readers a quick understanding of the article’s core subject.

Why it’s a geeky idea:

Advanced NLP Techniques: You’ll be working with techniques like Topic Modeling (e.g., LDA or BERTopic), Text Summarization (e.g., extractive or abstractive methods), and NER to automatically extract the essence of a text.

Real-World Application: This could be super useful for academic researchers, news aggregator, or even social media platforms where users often want a quick idea of what an article is about before deciding whether to engage with it.

Complexity: The system will need to handle nuances in language, extract contextual meaning, and be able to distinguish between various levels of detail (e.g., identifying the difference between an academic article, news piece, or opinion article).

Geek idea 2:

Smart Image Annotation & Retrieval System

A platform where users can upload images, annotate them with text and voice, and retrieve them using search or voice commands.

Tech Stack & Features

1. Image Annotation

✅ Text Annotations

Store images in Firebase Storage / AWS S3.

Store metadata (title, tags, descriptions) in a database (PostgreSQL, MongoDB, or Firebase Firestore).

Use a form in the UI for users to manually add text annotations.

✅ Voice Annotations

Record voice in the UI (use Web Audio API for browsers or React Native Audio Recorder for mobile).

Convert speech to text using OpenAI Whisper API or Google Speech-to-Text API.

Store both the original voice file and the converted text annotation.

2. AI Translation & Accessibility

✅ Automatic Translation

Use Google Translate API / DeepL API / OpenAI API to translate text annotations.

Store translations in the database alongside the original text.

✅ Text-to-Speech (TTS)

Convert translated text into speech using Google TTS API, ElevenLabs, or OpenAI.

Store and let users play the translated voice output.

✅ OCR Integration (Text Extraction from Images)

Use Tesseract OCR (open-source) or Google Vision API to extract text from images.

Automatically translate extracted text using AI translation APIs.

3. Smart Search & Retrieval

✅ Keyword-Based Search

Store tags, descriptions, and metadata in a structured way for search.

Use Elasticsearch, PostgreSQL full-text search, or Firebase Firestore search.

✅ Voice Search

Users speak a query → Convert to text with Google Speech-to-Text API → Match with stored image descriptions.

✅ Image Recognition (Auto-tagging & Reverse Search)

Use Google Vision API or OpenAI CLIP to auto-label images.

Enable reverse image search (find similar images based on content).

4. User Interface & Deployment

✅ Web & Mobile App Development

  • Frontend: React.js (for web) or React Native (for mobile).
  • Backend: FastAPI (Python) or Express.js (Node.js).

✅ Cloud Storage & Hosting

  • Store images in AWS S3 / Firebase Storage.
  • Host the backend on Firebase Functions, AWS Lambda, or Vercel.
  • Database: PostgreSQL (Supabase), MongoDB (Atlas), or Firebase Firestore.