Product-Package OCR Pipeline
A custom text-recognition pipeline that reliably extracts structured information from product packaging, where off-the-shelf OCR consistently failed.
Extracting reliable text from product packages — with their varied layouts, typefaces, colors, and image quality — was beyond what standard OCR could handle without producing noise-heavy output unsuitable for downstream pipelines.
Engineered a customized recognition model built on a modified FastText algorithm, with a bespoke pixel-intensity-variation detection, segmentation, and classification pipeline tuned specifically for product package imagery.
Reliable structured text extraction from product packaging feeding retail data pipelines. [Add throughput or accuracy figure if available.]
Off-the-shelf OCR is trained on clean document imagery. Product packaging is the opposite: high visual noise, overlapping type on complex backgrounds, irregular font choices, and photography that varies by supplier. Rather than attempting to clean inputs to fit a general model, the pipeline was designed around the specific characteristics of packaging imagery — pixel-intensity variation as a segmentation signal, classification tuned to the attribute vocabulary the business cared about.
Modifying FastText rather than choosing a heavier model was a deliberate engineering decision: the throughput requirements favored a lean, fast algorithm that could be tuned precisely over a more capable but opaque neural network that would be harder to maintain and explain to downstream consumers.
This is the clearest from-scratch ML engineering example in the portfolio — algorithm modification, custom pipeline, and domain-specific tuning rather than API integration.