Poker computer vision

How AI Reads Poker Streams: Names, Cards, Pots, And Actions

A poker livestream is full of structured information, but the structure is painted into the video. AI has to recover it from pixels.

Step 1: Find The Relevant Regions

Before software can read a stream, it needs to know where the important graphics are. That is stream calibration. The system identifies regions for player names, hole cards, stack sizes, board cards, pot size, and action graphics. Different poker streams use different layouts, so a reusable stream profile is more reliable than guessing every frame.

Step 2: Read Text With OCR

OCR converts on-screen text into machine-readable data. In poker, OCR can detect player names, pot sizes, stacks, and sometimes card labels. The hard part is that poker streams use compression, motion, colored graphics, shadows, and stylized fonts. The goal is not perfect document OCR. The goal is enough accuracy to find hands and build metadata.

Step 3: Detect Cards And Table State

Card recognition can use OCR, image matching, symbol recognition, or a hybrid approach. The system needs to understand when cards appear, disappear, change, or become hidden. It also has to handle timing: streams often delay reveals, cover folded cards, or animate graphics.

Step 4: Turn Frames Into Events

The real value comes when individual frames become events. A player appears in the hand. The flop arrives. The pot crosses a threshold. A target player reaches showdown. Once those events exist, a long video becomes a searchable timeline.

Step 5: Clip, Label, And Package

Poker Stream Clipper uses this general idea for a practical workflow: choose a stream, identify the relevant player or biggest pots, and generate clips instead of manually searching through hours of footage. The more metadata the system can recover, the more useful the clips become for players, creators, and analysts.