cwLeeDev/AIFlow-Math-Ink-0.5

AIFlow Math Ink 0.5

AIFlow Math Ink 0.5 is a model-agnostic geometric gridding layer for online handwritten mathematics. It converts ordered pen strokes into spatially coherent formula cells and renders one clean image per cell.

This repository contains no OCR model, model weight, tokenizer, training data, or generated prediction. It is a deterministic preprocessing layer only.

The output images can be passed to any image-to-LaTeX recognizer. They are interface-compatible with TexTeller-style image batches, but TexTeller is not included, redistributed, modified, or required by this repository.

Why this layer exists

An image-to-LaTeX model normally expects one coherent expression image. A pen canvas may instead contain multiple rows, detached superscripts, fractions, or matrix delimiters. AIFlow Math Ink 0.5 uses the original stroke geometry to partition that canvas before recognition.

The layer:

1. validates bounded UTF-8 JSON-like stroke events;
2. computes width-aware stroke bounding boxes;
3. estimates a scale from the median stroke height;
4. creates same-baseline and local-attachment edges;
5. adds structural bridge edges for fraction bars and tall delimiters;
6. extracts connected components with a disjoint-set structure;
7. sorts cells in stable two-dimensional reading order; and
8. re-renders only the member strokes of each cell.

This is a transparent heuristic algorithm, not a trained neural-network layer.

Installation

pip install Pillow

Clone this repository and add src to your Python path, or install it locally:

pip install -e .

Minimal example

from PIL import Image

from aiflow_math_ink_05 import GridConfig, Stroke, build_formula_grids, render_grid_images

strokes = [
    Stroke(0, ((24, 20), (36, 20)), 3.0),
    Stroke(1, ((10, 42), (60, 42)), 3.0),
    Stroke(2, ((24, 64), (36, 64)), 3.0),
]

grids = build_formula_grids(strokes, GridConfig())
images = render_grid_images(Image.new("RGB", (80, 90), "white"), strokes, grids)

for grid, image in zip(grids, images, strict=True):
    print(grid.stroke_ids)
    image.save(f"cell-{grid.index}.png")

For an existing API payload, use parse_writing_events() before build_formula_grids(). See examples/basic.py.

Recognizer compatibility

render_grid_images() returns ordinary RGB PIL.Image.Image objects. A recognizer can consume the list as a batch without importing this package into its model implementation. TexTeller compatibility means only this image-level interface; no TexTeller component is included.

Method and complexity

The complete method, equations, assumptions, and limitations are documented in docs/METHOD.md.

For (n) accepted strokes, pair construction is (O(n^2)), disjoint-set operations are effectively near-linear, and rendering is proportional to the number of retained points plus output pixels. Input caps are mandatory in server environments; the parser defaults to 2,000 strokes and 200,000 total points.

Scope and limitations

• This release performs geometry-based grouping, not symbol recognition.
• It does not infer LaTeX, semantics, or correctness.
• Thresholds are scale-adaptive but remain heuristic.
• Dense overlapping notes and unusual layouts can over-merge.
• The method has not been presented here as a peer-reviewed contribution.
• The repository ships no dataset and makes no benchmark claim.

License

The original code in this repository is licensed under Apache License 2.0. Pillow is an install-time dependency and is not vendored. External recognizers are separate works governed by their own licenses.