10.5.1 Advanced Vision Roadmap: OCR, Face, Video, 3D

Advanced vision is not a list of model names. It is a set of application directions built on the same visual foundation: more complex inputs, outputs, constraints, and risks.

See the Direction Map First

OCR fits documents, face recognition fits identity-sensitive scenarios, video fits time and motion, and 3D vision fits spatial structure.

Run a Direction Choice Check

Pick one direction instead of trying all four shallowly.

requirement = {
    "input": "screenshot",
    "needs_text": True,
    "needs_identity": False,
    "needs_time": False,
    "needs_depth": False,
}

if requirement["needs_text"]:
    direction = "OCR"
elif requirement["needs_identity"]:
    direction = "Face"
elif requirement["needs_time"]:
    direction = "Video"
elif requirement["needs_depth"]:
    direction = "3D"
else:
    direction = "Classification or detection"

print("direction:", direction)
print("first_output:", "text with layout")

Expected output:

direction: OCR
first_output: text with layout

For face, surveillance, medical, or identity projects, write privacy and usage boundaries before showing results.

Learn in This Order

Step	Direction	Practice Output
1	OCR	Extract text, layout, fields, confidence, failure samples
2	Face	Detect faces, explain threshold, privacy, and bias risks
3	Video	Track events across frames and record temporal failures
4	3D vision	Explain depth, point cloud, geometry, and sensor assumptions

Evidence to Keep

Keep this page’s proof of learning as a small evidence card:

Scenario Boundary

face, video, OCR, 3D, medical, or another vision scenario

Input Sample

source image/frame/document and the expected output type

Result Artifact

extracted text, tracked event, depth clue, diagnosis flag, or review note

Failure Check

privacy, lighting, temporal drift, layout, calibration, or domain risk

Expected Output

scenario-specific artifact with metric or human-review note

Pass Check

You pass this chapter when you choose one direction, define input/output, run a minimum project, and document failure cases plus usage boundaries.

Check reasoning and explanation

1. A passing answer maps the task to the right visual output: class label, bounding box, mask, OCR text, embedding, or video event.
2. The evidence should include a rendered visual artifact and one metric or qualitative error note.
3. A good self-check names one visual failure mode such as class confusion, missed objects, bad masks, lighting shift, domain shift, or weak annotation quality.