Abstract
A Python script that procedurally generates word-search puzzles from a supplied word list, places words in a configurable grid, fills remaining cells with random letters, and renders the result as a color-configurable image through OpenCV and Pillow. The interesting part is the hand-off between the placement library and the image-rendering pipeline, which turns a text grid into a shareable picture rather than console output.
1. What This Is
Built in March 2022 as a portfolio piece. The generator takes a word list, grid dimensions (10×10 and up), a difficulty setting, and color choices, then produces a rendered puzzle image that can be saved to disk or displayed via an OpenCV window. It separates puzzle rules (size, difficulty, word set) from presentation (colors, layout) so the same grid logic can feed different visual outputs.
2. How It Works
The pipeline is linear: configuration in, image out. A word-search generation library handles the placement and filler-letter logic; the script orchestrates the rest and hands the finished grid to Pillow/NumPy for pixel-level rendering.
| # | Stage | Input | Tool | Output |
|---|---|---|---|---|
| 01 | Configuration | Word list, grid size, difficulty, colors | Python | Parameter set |
| 02 | Word placement | Parameter set | Word-search library | Grid with placed words |
| 03 | Filler letters | Grid with gaps | Word-search library | Complete letter grid |
| 04 | Image rendering | Complete grid, color config | Pillow, NumPy | Pixel image buffer |
| 05 | Output | Image buffer | OpenCV | Saved file / display window |
3. Implementation Notes
3.1 Placement delegated to a library
The core word-placement and filler-letter logic is not hand-rolled; a dedicated word-search generation library handles it. This keeps the script focused on orchestration and rendering, but it also means placement quality (overlap handling, direction variety) is bounded by what that library exposes.
3.2 Rendering path
The grid is converted to a NumPy array, Pillow draws each letter at its cell position with the chosen font and color, and OpenCV handles the final save or window display. NumPy sits in the middle as the interchange format between the logical grid and the pixel buffer.
4. Constraints
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No solvability check
The script trusts the placement library to produce a valid puzzle. There is no independent solver pass to confirm every word is actually findable in the final grid.
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Single-puzzle output
One run produces one image. There is no batch mode, no PDF export, and no way to generate a set of puzzles with varying difficulty in a single invocation.
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Basic color configuration
Colors are set per run but there is no theme system, no contrast validation, and no support for print-specific palettes. The visual output is functional rather than polished.
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No tests or CI
The repository has no automated test suite. Grid-size edge cases (very small grids, very long words) are unverified beyond manual runs.
5. Next
- a. Add a lightweight solver pass that scans the rendered grid to verify every target word is present before the image is saved.
- b. Introduce a batch mode that accepts a difficulty range and emits multiple puzzles as a single PDF for printing.
- c. Replace the ad-hoc color parameters with a small theme dictionary (background, letter, highlight) and add a contrast check so the output remains legible in grayscale print.
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