Jigsaw Jeeves: Building a Puzzle Assistant using Computer Vision

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The article, written by Chinmay Kakatkar, presents Jigsaw Jeeves — a Python tool that assists (rather than fully solves) jigsaw puzzles by using computer vision to suggest likely piece placements and groupings. The deliberate choice to assist rather than automate entirely is well-reasoned: full automation would require handling arbitrary rotations, irregular shapes, and lighting variation across thousands of pieces. The implementation uses three core libraries: OpenCV for image processing, NumPy for array operations, and SciPy for optimization. The core framing — matching puzzle fragments to a reference image — maps directly to industrially relevant problems: satellite image tile stitching, forensic document reconstruction, manufacturing assembly verification, and art restoration. The tutorial covers feature extraction from piece images, similarity measurement between pieces and reference regions, and a divide-and-conquer approach to breaking a 5,000-piece problem into manageable subsets. The nudge philosophy (point toward a cluster, not solve it) maps cleanly to human-in-the-loop AI design principles. This is educational content rather than a research breakthrough. Its value is demonstrating how classical computer vision solves a concrete, relatable problem — with transferable patterns for practitioners building fragment-matching systems.