Technical Briefing
How OCR Pulse Works
A staged OCR pipeline walkthrough
OCR Pulse is a teaching-oriented pipeline with four stages: capture, binarize, segment, and recognize. It emphasizes how OCR systems progressively reduce raw pixels into structured input.
Data Pathway
Drawing
Processing
OCR Output
1) Binarization
The app thresholds RGB pixel values into black/white to remove color noise and simplify shape extraction.
Thresholding step
for (let i = 0; i < data.length; i += 4) {
const avg = (data[i] + data[i + 1] + data[i + 2]) / 3;
const val = avg > 50 ? 255 : 0;
data[i] = data[i + 1] = data[i + 2] = val;
}2) Region-of-interest detection
A min/max scan over active pixels finds the bounding box around the drawn digit.
Bounding box scan
let minX = width, minY = height, maxX = 0, maxY = 0;
for (let y = 0; y < height; y++) {
for (let x = 0; x < width; x++) {
const i = (y * width + x) * 4;
if (data[i] > 128) {
minX = Math.min(minX, x);
minY = Math.min(minY, y);
maxX = Math.max(maxX, x);
maxY = Math.max(maxY, y);
}
}
}3) Recognition stage
The current implementation uses a simulated result with visual 'thinking' feedback, but it is structured so a TF.js digit model can be plugged in later.
Mission Debrief
Great conceptual map of OCR preprocessing.
Shows where ROI extraction fits in the pipeline.
Ready for swapping in a real classifier backend.