Product catalogues look simple to sighted readers, but they create hard remediation problems around reading order, product grouping and image deduplication.
For teams comparing remediation quality on retail, product or brochure-style PDFs.
A catalogue page is not a table, even when it looks like a grid.
Product names, descriptions, prices, stock codes and images must stay together.
Repeated decorative images should not overwhelm a screen-reader user.
The visual layout hides the logical problem
A sighted reader scans a catalogue by product cell. A screen reader needs a logical sequence. If remediation reads across the page without grouping, it may announce three product names, then three prices, then unrelated stock codes.
That turns a visually clear page into an unusable audio experience.
Product grouping is the core challenge
A good remediation pass must keep each product unit together. The image, name, short description, price and stock code should form one meaningful block before the next product begins.
Detect product cells without relying on visible grid lines.
Suppress decorative repetition without losing meaningful product images.
Avoid duplicate alt text where a product description already communicates the same content.
Keep price and stock code association intact.
Why this matters commercially
Catalogues are a useful proof case because they expose whether a remediation system understands layout or only extracts text. If a system can handle catalogues, it is more likely to handle real marketing, procurement and public-information documents.
Catalogue remediation quality checks
Can a screen-reader user hear one complete product before the next?
Are stock codes associated with the right product?
Are prices announced with the right item?
Are decorative images suppressed where appropriate?
Are meaningful product images described without creating noise?
Frequently asked questions
Why not convert catalogues into plain text?
Plain text can lose product relationships. The goal is not only text extraction, but preserving the meaning created by the visual layout.
Can AI help with catalogue remediation?
Yes, when it is used to infer visual grouping and context. Generic OCR alone is not enough.