Search, citation context, full text and AI synthesis
Keep these strengths as the main research-information experience.
For literature databases, publishers, scientific AI and research-information platforms
Your platform understands the paper. Let the user work with the FTIR spectrum inside the figure.
Research-information products are already strong at full text, citations, references, summaries and evidence navigation. FTIR.fun turns an FTIR figure into numerical spectrum data that can be quality-checked, compared with the reader's own spectrum, interpreted at band level and reused downstream.
Where FTIR.fun is different
Paper figure → numerical spectrum → extraction QC → own-data comparison → peak evidence → reusable scientific object. The value is not another paper summary; it is making the spectrum itself computable.
The numerical FTIR spectrum inside the figure
When raw XY data are absent, the reader cannot normally compute against the spectrum shown in the paper.
A spectroscopy workflow after figure understanding
Recover the curve, validate the extraction, compare it with experimental data, inspect changed bands and return the spectrum plus evidence to the host product.
Literature Spectrum Guide
Turn a paper figure into data you can directly compare with your experiment
1
Add the paper figure
Start from the FTIR figure or panel and retain DOI, article and figure identity so the recovered spectrum never loses its source context.
2
Recover the numerical curve
Detect the FTIR axes and curve, recover dense XY points and generate structured spectrum data instead of a screenshot or manually copied peak list.
3
Check extraction quality
Review axis interpretation, selected curve, overlay and redraw agreement. Multi-curve, offset, labelled, low-resolution or ambiguous figures enter a review path rather than silently producing a trusted CSV.
4
Compare with your own spectrum
Overlay the recovered literature spectrum with experimental data and calculate the meaningful shifts, new or missing bands, intensity differences and other spectral changes.
5
Interpret and export
Explain the important differences with FTIR-specific reference and literature evidence, then export the recovered CSV, comparison figure, changed-band findings, provenance and peak-level evidence for research or downstream platform use.
Competitive difference
Complement literature AI and generic chart digitizers by making the recovered FTIR spectrum scientifically usable
| Platform task | Typical literature AI / database / digitizer | FTIR.fun difference |
|---|---|---|
| Full-text and citation intelligence | Research platforms are broader and stronger; this is their core business. | Uses the paper context but concentrates on the spectrum as numerical scientific data. |
| Read figure caption | General scientific AI can summarize the caption and surrounding discussion. | Extracts and reasons over the plotted curve itself. |
| Digitize x/y coordinates | Generic digitizers support many chart types. | Adds FTIR axis conventions, spectrum-oriented output, extraction QC, provenance and downstream spectroscopy analysis. |
| Paper spectrum vs user spectrum | Usually outside citation and literature-search workflows. | Makes recovered paper data immediately available for numerical overlay, difference analysis and band-level interpretation. |
| Scientific evidence | Literature systems know the article and citation graph. | Connects DOI / figure provenance to peak-level spectral evidence and the user's own experimental comparison. |
| Reusable output | The figure often remains only visual content in the paper record. | Returns structured spectrum data, extraction quality, comparison artifacts and evidence for search, writing, agent or database workflows. |
A feature a research-platform user understands immediately
Open a paper, select an FTIR figure, recover the numerical spectrum, upload your own experimental spectrum and receive a direct comparison with peak shifts, new or missing bands and supporting evidence. The platform moves from explaining what the authors wrote about the spectrum to letting the reader work with the spectrum itself.
Literature-platform PoC
Test representative papers, including difficult figures
Measure extraction accuracy, automatic completion rate, review burden, provenance quality and whether the recovered spectrum creates a downstream research action that the platform's users value.
- Representative FTIR figures, including multi-curve and low-quality cases
- DOI / article / figure identifiers
- Expected downstream user action
- Experimental spectra for paper-to-own-data comparison