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Landing AI

A hosted PDF-to-markdown API that extracts tables, charts, signatures, and scanned text well, but flattens some heading hierarchy.

Hybrid PDFsTable extractionChart summariesSignature attestations
TL;DR — our verdictUpdated August 2026 · 17 test artifacts

Strong extraction, uneven structure

Where it wins
  • You need a hosted API that returns downloadable markdown from complex PDFs without manual cleanup.
  • You work with hybrid PDFs that mix native text, tables, charts, signatures, and scanned pages.
  • You want financial tables and charts turned into readable markdown or text instead of being dropped.
Main limitation
  • You need exact heading-level fidelity, because major headings were flattened in the hybrid and financial reports.

Our take

Landing AI is a solid fit when you need to turn messy PDFs into usable markdown with minimal handholding. Across the hybrid earnings report, the table-heavy financial report, and the scanned research paper, it accepted every file, returned downloadable markdown, kept much of the reading order intact, reconstructed tables well enough to read, and turned charts and signature blocks into semantic text. The main tradeoff is structural fidelity: some major headings were flattened, nested table headers were compressed, and scanned-page hierarchy still needed review.

Download video: Demo video
Hybrid PDF extraction walkthrough showing the markdown export flow.

In-Depth Review

Our detailed analysis of Landing AI — features, performance, and real-world testing.

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AI Demos Team
Expert Reviewer
Verified Review

Feature-by-Feature Breakdown

Automated Markdown Export
Reliable markdown output
▾
Test Summary
Feature tested: Automated Markdown Export
Result: Passed — Reliable markdown output

Feature tested: Automated Markdown Export

Result: Passed

Verdict: Reliable markdown output

Expected behavior: Landing AI converts complex PDFs into downloadable markdown files automatically, with readable structure preserved on annual-report, financial-report, and scanned two-column research PDF inputs. The grouped cards show the same end-to-end markdown export behavior under two near-synonymous titles.

Test case: PDF document → Text/code file

Input type: PDF document

Input used: Input artifact (PDF document): Hybrid earnings report with native text, tables, charts, and a scanned signature page. — Hybrid-Earnings-PDF.pdf

Observed output: Output artifact (Text/code file): Returned a downloadable markdown file through the API with no manual correction required. — landingai_hybrid_earningspdf_output.md

Input artifact: Input artifact (PDF document): Hybrid earnings report with native text, tables, charts, and a scanned signature page. — Hybrid-Earnings-PDF.pdf

Output artifact: Output artifact (Text/code file): Returned a downloadable markdown file through the API with no manual correction required. — landingai_hybrid_earningspdf_output.md

What changed: PDF document transformed into Text/code file

Test case: PDF document → Text/code file

Input type: PDF document

Input used: Input artifact (PDF document): Table-heavy financial report tested for API-based markdown export. — Sumitomo Financial PDF.pdf

Observed output: Output artifact (Text/code file): Returned parsed markdown as a downloadable file from the automated flow. — landingai_financialpdf_output.md

Input artifact: Input artifact (PDF document): Table-heavy financial report tested for API-based markdown export. — Sumitomo Financial PDF.pdf

Output artifact: Output artifact (Text/code file): Returned parsed markdown as a downloadable file from the automated flow. — landingai_financialpdf_output.md

What changed: PDF document transformed into Text/code file

Test case: PDF document → Text/code file

Input type: PDF document

Input used: Input artifact (PDF document): Scanned research paper used to test OCR plus markdown export. — Scanned Research PDF.pdf

Observed output: Output artifact (Text/code file): Returned parsed markdown for the scanned document without any manual post-processing. — landingai_scannedpdf_output.md

Input artifact: Input artifact (PDF document): Scanned research paper used to test OCR plus markdown export. — Scanned Research PDF.pdf

Output artifact: Output artifact (Text/code file): Returned parsed markdown for the scanned document without any manual post-processing. — landingai_scannedpdf_output.md

What changed: PDF document transformed into Text/code file

Why it matters / Conclusion: Strong automation fit for direct markdown ingestion.

Landing AI converts complex PDFs into downloadable markdown files automatically, with readable structure preserved on annual-report, financial-report, and scanned two-column research PDF inputs. The grouped cards show the same end-to-end markdown export behavior under two near-synonymous titles.

pdf
Hybrid-Earnings-PDF.pdf
Hybrid earnings report with native text, tables, charts, and a scanned signature page.
↓
text
landingai_hybrid_earningspdf_output.md
Loading file...
Returned a downloadable markdown file through the API with no manual correction required.
pdf
Sumitomo Financial PDF.pdf
Table-heavy financial report tested for API-based markdown export.
↓
text
landingai_financialpdf_output.md
Loading file...
Returned parsed markdown as a downloadable file from the automated flow.
pdf
Scanned Research PDF.pdf
Scanned research paper used to test OCR plus markdown export.
↓
text
landingai_scannedpdf_output.md
Loading file...
Returned parsed markdown for the scanned document without any manual post-processing.
Bottom Line
Strong automation fit for direct markdown ingestion.
Reading Order Reconstruction
Mixed structure fidelity
▾
Test Summary
Feature tested: Reading Order Reconstruction
Result: Partial — Mixed structure fidelity

Feature tested: Reading Order Reconstruction

Result: Partial

Verdict: Mixed structure fidelity

Expected behavior: Landing AI reconstructs headings, paragraphs, and page flow into a readable sequence across the tested PDFs, including the commitments section and financial-report hierarchy. The evidence differs only by duplicate phrasing of the same ordering behavior.

Test case: Image → Image

Input type: Image

Input used: Input artifact (Image): Target annual report commitments-and-contingencies section on the data-breach disclosure. — hybrid_earningspdf_commitments_section.png

Observed output: Output artifact (Image): Kept the section readable and in order, preserving the heading/content relationship within the disclosure. — landingai_hybrid_earningspdf_parsed_commitments_section.png

Input artifact: Input artifact (Image): Target annual report commitments-and-contingencies section on the data-breach disclosure. — hybrid_earningspdf_commitments_section.png

Output artifact: Output artifact (Image): Kept the section readable and in order, preserving the heading/content relationship within the disclosure. — landingai_hybrid_earningspdf_parsed_commitments_section.png

What changed: Image transformed into Image

Test case: Image → Image

Input type: Image

Input used: Input artifact (Image): Target annual report page 3 with the 'A Growth Story Again' opening page and multi-column narrative. — earnings_hybrid_pdf_input_page_3.png

Observed output: Output artifact (Image): Flattened the top-level page heading into plain text rather than a true H1, reducing hierarchy fidelity. — landingai_hybrid_earningspdf_parsed_doc_hierarchy.png

Input artifact: Input artifact (Image): Target annual report page 3 with the 'A Growth Story Again' opening page and multi-column narrative. — earnings_hybrid_pdf_input_page_3.png

Output artifact: Output artifact (Image): Flattened the top-level page heading into plain text rather than a true H1, reducing hierarchy fidelity. — landingai_hybrid_earningspdf_parsed_doc_hierarchy.png

What changed: Image transformed into Image

Test case: Image → Image

Input type: Image

Input used: Input artifact (Image): Financial report page 6 with dense narrative and performance figures. — financial_pdf_document_page_6.png

Observed output: Output artifact (Image): Preserved the section heading and paragraph order in a readable hierarchy. — landingai_financialpdf_parsed_hierarchy.png

Input artifact: Input artifact (Image): Financial report page 6 with dense narrative and performance figures. — financial_pdf_document_page_6.png

Output artifact: Output artifact (Image): Preserved the section heading and paragraph order in a readable hierarchy. — landingai_financialpdf_parsed_hierarchy.png

What changed: Image transformed into Image

Test case: Image → Image

Input type: Image

Input used: Input artifact (Image): Scanned research paper title page and opening abstract section. — scanned_pdf_page_1.png

Observed output: Output artifact (Image): Recovered the opening content, but the title-and-surrounding-content relationship was not semantically clean. — landingai_scannedpdf_parsed_page1.png

Input artifact: Input artifact (Image): Scanned research paper title page and opening abstract section. — scanned_pdf_page_1.png

Output artifact: Output artifact (Image): Recovered the opening content, but the title-and-surrounding-content relationship was not semantically clean. — landingai_scannedpdf_parsed_page1.png

What changed: Image transformed into Image

Test case: Image → Image

Input type: Image

Input used: Input artifact (Image): Scanned two-column study-area section from the forestry paper. — scanned_pdf_multicolumn_section.png

Observed output: Output artifact (Image): Reconstructed the multi-column section in a readable order while keeping the section structure visible. — landingai_scannedpdf_parsed_hierarchy.png

Input artifact: Input artifact (Image): Scanned two-column study-area section from the forestry paper. — scanned_pdf_multicolumn_section.png

Output artifact: Output artifact (Image): Reconstructed the multi-column section in a readable order while keeping the section structure visible. — landingai_scannedpdf_parsed_hierarchy.png

What changed: Image transformed into Image

Why it matters / Conclusion: Useful for readable extraction, but not safe if exact heading semantics matter.

Landing AI reconstructs headings, paragraphs, and page flow into a readable sequence across the tested PDFs, including the commitments section and financial-report hierarchy. The evidence differs only by duplicate phrasing of the same ordering behavior.

image
Input artifact for "Reading Order Reconstruction" test: Target annual report commitments-and-contingencies section on the data-breach disclosure., hybrid_earningspdf_commitments_section.png
Target annual report commitments-and-contingencies section on the data-breach disclosure.
↓
image
Output artifact for "Reading Order Reconstruction" test: Kept the section readable and in order, preserving the heading/content relationship within the disclosure., landingai_hybrid_earningspdf_parsed_commitments_section.png
Kept the section readable and in order, preserving the heading/content relationship within the disclosure.
image
Input artifact for "Reading Order Reconstruction" test: Target annual report page 3 with the 'A Growth Story Again' opening page and multi-column narrative., earnings_hybrid_pdf_input_page_3.png
Target annual report page 3 with the 'A Growth Story Again' opening page and multi-column narrative.
↓
image
Output artifact for "Reading Order Reconstruction" test: Flattened the top-level page heading into plain text rather than a true H1, reducing hierarchy fidelity., landingai_hybrid_earningspdf_parsed_doc_hierarchy.png
Flattened the top-level page heading into plain text rather than a true H1, reducing hierarchy fidelity.
image
Input artifact for "Reading Order Reconstruction" test: Financial report page 6 with dense narrative and performance figures., financial_pdf_document_page_6.png
Financial report page 6 with dense narrative and performance figures.
↓
image
Output artifact for "Reading Order Reconstruction" test: Preserved the section heading and paragraph order in a readable hierarchy., landingai_financialpdf_parsed_hierarchy.png
Preserved the section heading and paragraph order in a readable hierarchy.
image
Input artifact for "Reading Order Reconstruction" test: Scanned research paper title page and opening abstract section., scanned_pdf_page_1.png
Scanned research paper title page and opening abstract section.
↓
image
Output artifact for "Reading Order Reconstruction" test: Recovered the opening content, but the title-and-surrounding-content relationship was not semantically clean., landingai_scannedpdf_parsed_page1.png
Recovered the opening content, but the title-and-surrounding-content relationship was not semantically clean.
image
Input artifact for "Reading Order Reconstruction" test: Scanned two-column study-area section from the forestry paper., scanned_pdf_multicolumn_section.png
Scanned two-column study-area section from the forestry paper.
↓
image
Output artifact for "Reading Order Reconstruction" test: Reconstructed the multi-column section in a readable order while keeping the section structure visible., landingai_scannedpdf_parsed_hierarchy.png
Reconstructed the multi-column section in a readable order while keeping the section structure visible.
Bottom Line
Useful for readable extraction, but not safe if exact heading semantics matter.
Table Reconstruction
Strong on clean tables; mixed on scans
▾
Test Summary
Feature tested: Table Reconstruction
Result: Partial — Strong on clean tables; mixed on scans

Feature tested: Table Reconstruction

Result: Partial

Verdict: Strong on clean tables; mixed on scans

Expected behavior: Landing AI rebuilds readable tables from both born-digital and scanned PDFs, preserving rows, columns, and much of the value structure. The tests include dense financial tables, nested or multi-level headers, segment comparison tables, and scanned cells split by intervening text.

Test case: Image → Image

Input type: Image

Input used: Input artifact (Image): Target annual report financial summary table spanning 2011 to 2015. — earnings_hybridInput_table.png

Observed output: Output artifact (Image): Reconstructed a readable financial summary table with aligned rows, columns, and year values. — landingai_hybrid_earnings_pdf_parsed_table.png

Input artifact: Input artifact (Image): Target annual report financial summary table spanning 2011 to 2015. — earnings_hybridInput_table.png

Output artifact: Output artifact (Image): Reconstructed a readable financial summary table with aligned rows, columns, and year values. — landingai_hybrid_earnings_pdf_parsed_table.png

What changed: Image transformed into Image

Test case: Image → Image

Input type: Image

Input used: Input artifact (Image): Segment comparison table with previous and present first-quarter orders received. — financial_pdf_multilevel_table.png

Observed output: Output artifact (Image): Kept the table readable, but the nested header structure was simplified. — landingai_financialpdf_parsed_multicolumn_pdf.png

Input artifact: Input artifact (Image): Segment comparison table with previous and present first-quarter orders received. — financial_pdf_multilevel_table.png

Output artifact: Output artifact (Image): Kept the table readable, but the nested header structure was simplified. — landingai_financialpdf_parsed_multicolumn_pdf.png

What changed: Image transformed into Image

Test case: Image → Image

Input type: Image

Input used: Input artifact (Image): Multi-level segment reporting table with subtotal, other, total, and adjustment columns. — financial_pdf_complex_table.png

Observed output: Output artifact (Image): Preserved the overall segment reporting layout and values, though the nested header levels were compressed. — landingai_financialpdf_parsed_multilevel_table.png

Input artifact: Input artifact (Image): Multi-level segment reporting table with subtotal, other, total, and adjustment columns. — financial_pdf_complex_table.png

Output artifact: Output artifact (Image): Preserved the overall segment reporting layout and values, though the nested header levels were compressed. — landingai_financialpdf_parsed_multilevel_table.png

What changed: Image transformed into Image

Test case: Image → Image

Input type: Image

Input used: Input artifact (Image): Scanned lodgepole pine table showing stand structure before and after cutting. — scanned_pdf_complex table.png

Observed output: Output artifact (Image): Recovered the stand-structure table from a scan, but OCR quality was uneven in the dense layout. — landingai_scannedpdf_parsed_complex_table.png

Input artifact: Input artifact (Image): Scanned lodgepole pine table showing stand structure before and after cutting. — scanned_pdf_complex table.png

Output artifact: Output artifact (Image): Recovered the stand-structure table from a scan, but OCR quality was uneven in the dense layout. — landingai_scannedpdf_parsed_complex_table.png

What changed: Image transformed into Image

Test case: Image → Image

Input type: Image

Input used: Input artifact (Image): Scanned table where vertical text interrupts the columns. — scanned_pdf_table_with_text_between_columns.png

Observed output: Output artifact (Image): Recovered the table structure, but intervening text split the columns and introduced OCR corruption. — landingai_scannedpdf_parsed_multicolumn_table_with_intervening_text.png

Input artifact: Input artifact (Image): Scanned table where vertical text interrupts the columns. — scanned_pdf_table_with_text_between_columns.png

Output artifact: Output artifact (Image): Recovered the table structure, but intervening text split the columns and introduced OCR corruption. — landingai_scannedpdf_parsed_multicolumn_table_with_intervening_text.png

What changed: Image transformed into Image

Why it matters / Conclusion: Best on clean financial tables; scanned or nested tables need review.

Landing AI rebuilds readable tables from both born-digital and scanned PDFs, preserving rows, columns, and much of the value structure. The tests include dense financial tables, nested or multi-level headers, segment comparison tables, and scanned cells split by intervening text.

image
Input artifact for "Table Reconstruction" test: Target annual report financial summary table spanning 2011 to 2015., earnings_hybridInput_table.png
Target annual report financial summary table spanning 2011 to 2015.
↓
image
Output artifact for "Table Reconstruction" test: Reconstructed a readable financial summary table with aligned rows, columns, and year values., landingai_hybrid_earnings_pdf_parsed_table.png
Reconstructed a readable financial summary table with aligned rows, columns, and year values.
image
Input artifact for "Table Reconstruction" test: Segment comparison table with previous and present first-quarter orders received., financial_pdf_multilevel_table.png
Segment comparison table with previous and present first-quarter orders received.
↓
image
Output artifact for "Table Reconstruction" test: Kept the table readable, but the nested header structure was simplified., landingai_financialpdf_parsed_multicolumn_pdf.png
Kept the table readable, but the nested header structure was simplified.
image
Input artifact for "Table Reconstruction" test: Multi-level segment reporting table with subtotal, other, total, and adjustment columns., financial_pdf_complex_table.png
Multi-level segment reporting table with subtotal, other, total, and adjustment columns.
↓
image
Output artifact for "Table Reconstruction" test: Preserved the overall segment reporting layout and values, though the nested header levels were compressed., landingai_financialpdf_parsed_multilevel_table.png
Preserved the overall segment reporting layout and values, though the nested header levels were compressed.
image
Input artifact for "Table Reconstruction" test: Scanned lodgepole pine table showing stand structure before and after cutting., scanned_pdf_complex table.png
Scanned lodgepole pine table showing stand structure before and after cutting.
↓
image
Output artifact for "Table Reconstruction" test: Recovered the stand-structure table from a scan, but OCR quality was uneven in the dense layout., landingai_scannedpdf_parsed_complex_table.png
Recovered the stand-structure table from a scan, but OCR quality was uneven in the dense layout.
image
Input artifact for "Table Reconstruction" test: Scanned table where vertical text interrupts the columns., scanned_pdf_table_with_text_between_columns.png
Scanned table where vertical text interrupts the columns.
↓
image
Output artifact for "Table Reconstruction" test: Recovered the table structure, but intervening text split the columns and introduced OCR corruption., landingai_scannedpdf_parsed_multicolumn_table_with_intervening_text.png
Recovered the table structure, but intervening text split the columns and introduced OCR corruption.
Bottom Line
Best on clean financial tables; scanned or nested tables need review.
Chart Content Extraction
Preserves chart meaning in text
▾
Test Summary
Feature tested: Chart Content Extraction
Result: Passed — Preserves chart meaning in text

Feature tested: Chart Content Extraction

Result: Passed

Verdict: Preserves chart meaning in text

Expected behavior: Landing AI turns charts into descriptive text that retains titles, labels, approximate values, and trend direction rather than preserving chart artwork. The tested inputs included an SG&A waterfall chart and a scanned tree-mortality bar chart.

Test case: Image → Image

Input type: Image

Input used: Input artifact (Image): Waterfall chart showing the Selling, General and Administrative Expense Rate from 2013 to 2015. — hybrid_earningspdf_sga_chart.png

Observed output: Output artifact (Image): Converted the waterfall into text that preserved the rate changes and contributing factors. — landingai_hybrid_earnigspdf_parsed_sga_chart.png

Input artifact: Input artifact (Image): Waterfall chart showing the Selling, General and Administrative Expense Rate from 2013 to 2015. — hybrid_earningspdf_sga_chart.png

Output artifact: Output artifact (Image): Converted the waterfall into text that preserved the rate changes and contributing factors. — landingai_hybrid_earnigspdf_parsed_sga_chart.png

What changed: Image transformed into Image

Test case: Image → Image

Input type: Image

Input used: Input artifact (Image): Bar chart showing tree mortality by year and cut treatment. — scanned_pdf_chart.png

Observed output: Output artifact (Image): Recovered the legend, approximate values, and trend relationships in text form. — landingai_scannedpdf_parsed_chart.png

Input artifact: Input artifact (Image): Bar chart showing tree mortality by year and cut treatment. — scanned_pdf_chart.png

Output artifact: Output artifact (Image): Recovered the legend, approximate values, and trend relationships in text form. — landingai_scannedpdf_parsed_chart.png

What changed: Image transformed into Image

Why it matters / Conclusion: Good for preserving chart meaning in text, not chart artwork.

Landing AI turns charts into descriptive text that retains titles, labels, approximate values, and trend direction rather than preserving chart artwork. The tested inputs included an SG&A waterfall chart and a scanned tree-mortality bar chart.

image
Input artifact for "Chart Content Extraction" test: Waterfall chart showing the Selling, General and Administrative Expense Rate from 2013 to 2015., hybrid_earningspdf_sga_chart.png
Waterfall chart showing the Selling, General and Administrative Expense Rate from 2013 to 2015.
↓
image
Output artifact for "Chart Content Extraction" test: Converted the waterfall into text that preserved the rate changes and contributing factors., landingai_hybrid_earnigspdf_parsed_sga_chart.png
Converted the waterfall into text that preserved the rate changes and contributing factors.
image
Input artifact for "Chart Content Extraction" test: Bar chart showing tree mortality by year and cut treatment., scanned_pdf_chart.png
Bar chart showing tree mortality by year and cut treatment.
↓
image
Output artifact for "Chart Content Extraction" test: Recovered the legend, approximate values, and trend relationships in text form., landingai_scannedpdf_parsed_chart.png
Recovered the legend, approximate values, and trend relationships in text form.
Bottom Line
Good for preserving chart meaning in text, not chart artwork.
Signature and Stamp Detection
Good at semantic attestation extraction
▾
Test Summary
Feature tested: Signature and Stamp Detection
Result: Passed — Good at semantic attestation extraction

Feature tested: Signature and Stamp Detection

Result: Passed

Verdict: Good at semantic attestation extraction

Expected behavior: Landing AI identifies signature blocks, signer metadata, and stamp-like attestation marks in document regions. The evidence came from a signatures page with names, titles, and dates, plus a blurry Ernst & Young LLP mark and other attestation-style regions.

Test case: Image → Image

Input type: Image

Input used: Input artifact (Image): Target annual report signatures page with legal certification text and handwritten signatures. — hybrid_earningspdf_signatures.png

Observed output: Output artifact (Image): Extracted signature attestations for the signers, including names, titles, and dates. — landingai_hybrid_earningspdf_parsed_signs.png

Input artifact: Input artifact (Image): Target annual report signatures page with legal certification text and handwritten signatures. — hybrid_earningspdf_signatures.png

Output artifact: Output artifact (Image): Extracted signature attestations for the signers, including names, titles, and dates. — landingai_hybrid_earningspdf_parsed_signs.png

What changed: Image transformed into Image

Test case: Image → Image

Input type: Image

Input used: Input artifact (Image): Blurry handwritten Ernst & Young LLP mark on a white background. — hybrid_earningspdf_blurry_stamp.png

Observed output: Output artifact (Image): Represented the mark as a stylized digital-signature-style attestation instead of dropping it. — landingai_hybrid_earningspdf_parsed_stamp.png

Input artifact: Input artifact (Image): Blurry handwritten Ernst & Young LLP mark on a white background. — hybrid_earningspdf_blurry_stamp.png

Output artifact: Output artifact (Image): Represented the mark as a stylized digital-signature-style attestation instead of dropping it. — landingai_hybrid_earningspdf_parsed_stamp.png

What changed: Image transformed into Image

Why it matters / Conclusion: Reliable at marking the presence of signature and stamp regions, but not a handwriting OCR tool.

Landing AI identifies signature blocks, signer metadata, and stamp-like attestation marks in document regions. The evidence came from a signatures page with names, titles, and dates, plus a blurry Ernst & Young LLP mark and other attestation-style regions.

image
Input artifact for "Signature and Stamp Detection" test: Target annual report signatures page with legal certification text and handwritten signatures., hybrid_earningspdf_signatures.png
Target annual report signatures page with legal certification text and handwritten signatures.
↓
image
Output artifact for "Signature and Stamp Detection" test: Extracted signature attestations for the signers, including names, titles, and dates., landingai_hybrid_earningspdf_parsed_signs.png
Extracted signature attestations for the signers, including names, titles, and dates.
image
Input artifact for "Signature and Stamp Detection" test: Blurry handwritten Ernst & Young LLP mark on a white background., hybrid_earningspdf_blurry_stamp.png
Blurry handwritten Ernst & Young LLP mark on a white background.
↓
image
Output artifact for "Signature and Stamp Detection" test: Represented the mark as a stylized digital-signature-style attestation instead of dropping it., landingai_hybrid_earningspdf_parsed_stamp.png
Represented the mark as a stylized digital-signature-style attestation instead of dropping it.
Bottom Line
Reliable at marking the presence of signature and stamp regions, but not a handwriting OCR tool.
✓ Use This If
●You need a hosted API that returns downloadable markdown from complex PDFs without manual cleanup.
●You work with hybrid PDFs that mix native text, tables, charts, signatures, and scanned pages.
●You want financial tables and charts turned into readable markdown or text instead of being dropped.
●You need signature blocks and stamps represented semantically in the extracted output.
✕ Skip This If
●You need exact heading-level fidelity, because major headings were flattened in the hybrid and financial reports.
●You need nested table headers preserved perfectly, because the complex financial table compressed header levels.
●You need flawless OCR and layout on scanned pages with intervening text, because the scanned research paper and scanned table showed hierarchy and OCR issues.
●You need the original chart visuals preserved as images, because the tool converts charts into text summaries instead.
developer-toolsapistext
In this report, all three PDFs were accepted by the API and returned as downloadable markdown files, with no manual correction or post-processing required.
Born-digital financial tables were reconstructed well enough to remain readable, but a complex multi-level table compressed nested headers, and the scanned table with intervening text showed OCR corruption.
Not consistently. The commitments section and some body hierarchies stayed readable, but major headings were flattened on the hybrid earnings page and the scanned report’s opening structure was misread.
It extracted scanned text and multi-column structure, but the scanned research paper’s opening hierarchy and one table with text between columns were misread, so scanned-page OCR still needs review.
It does not preserve charts visually in the extracted output. Instead, it turns charts into text summaries that keep labels, trend changes, and approximate values.
Yes. The report shows semantic attestation elements for signed pages and for a blurry Ernst & Young LLP mark, preserving the presence and character of the mark.
No. The research artifacts do not include any pricing or plan information.

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