AI Roulette Betting Automator
Budget: $30 – $250 USD
I’m building a proof-of-concept that watches a live online-casino roulette table, recognises each winning number directly from the streamed video, and immediately places the next bet according to a colour-based Martingale sequence. The aim is not to print money but to test and refine betting ideas, so accuracy and repeatability matter more than aggressive staking.
Here’s the flow I have in mind:
• Computer-vision module (OpenCV, Tesseract, or another proven stack) captures the wheel’s history panel or number ticker, then converts it to a clean numeric result with at least 95 % reliability.
• A lightweight ML layer (you may use a simple classifier or CNN you’ve trained) should handle variable fonts, dealer hands occasionally blocking the view, and different casino themes.
• Decision logic: if the last result is red, double the stake on black (and vice-versa) until recovery, following classic Martingale rules. All parameters—base chip size, stop-loss, number of steps—must be editable in a config file.
• Betting execution must interact with the browser DOM or the game’s HTML5 canvas directly; no hard-coded autoclicker coordinates. Selenium, Playwright, or a custom JavaScript injection are all acceptable so long as the solution adapts to table resizing.
Deliverables
1. Well-commented source code (Python preferred, but I’m open).
2. A short model-training notebook or script showing how the number-recognition network was produced.
3. Setup guide and a video or live call demonstration proving the bot can (a) read live spins with ≥95 % accuracy, and (b) place ten consecutive bets on the correct colour without manual input.
If you’ve previously handled OCR or computer-vision projects in dynamic, low-light streams, you’ll likely breeze through this. Let me know which libraries you’d lean on and any clever tricks you’ve used to stabilise recognition under flickering casino lighting.
Here’s the flow I have in mind:
• Computer-vision module (OpenCV, Tesseract, or another proven stack) captures the wheel’s history panel or number ticker, then converts it to a clean numeric result with at least 95 % reliability.
• A lightweight ML layer (you may use a simple classifier or CNN you’ve trained) should handle variable fonts, dealer hands occasionally blocking the view, and different casino themes.
• Decision logic: if the last result is red, double the stake on black (and vice-versa) until recovery, following classic Martingale rules. All parameters—base chip size, stop-loss, number of steps—must be editable in a config file.
• Betting execution must interact with the browser DOM or the game’s HTML5 canvas directly; no hard-coded autoclicker coordinates. Selenium, Playwright, or a custom JavaScript injection are all acceptable so long as the solution adapts to table resizing.
Deliverables
1. Well-commented source code (Python preferred, but I’m open).
2. A short model-training notebook or script showing how the number-recognition network was produced.
3. Setup guide and a video or live call demonstration proving the bot can (a) read live spins with ≥95 % accuracy, and (b) place ten consecutive bets on the correct colour without manual input.
If you’ve previously handled OCR or computer-vision projects in dynamic, low-light streams, you’ll likely breeze through this. Let me know which libraries you’d lean on and any clever tricks you’ve used to stabilise recognition under flickering casino lighting.