Landing AI
A capable PDF-to-markdown API for complex financial and scanned PDFs, with strong table and chart extraction but inconsistent heading semantics.
Best when table fidelity matters more than markdown semantics
Landing AI handled the core PDF-to-markdown job well across hybrid, table-heavy, and scanned documents: it accepted all tested files, returned downloadable markdown through a fully automated API flow, reconstructed several financial tables cleanly, and converted charts into usable text summaries instead of dropping them. The tradeoff is structure fidelity at the semantic level: major headings were not consistently preserved as headings, some nested table headers were flattened, and scanned table OCR could introduce value errors. It looks useful for ingestion pipelines that need broad document coverage, but not for users who need exact markdown hierarchy or perfect scanned-table accuracy.
In-Depth Review
Our detailed analysis of Landing AI — features, performance, and real-world testing.
Feature-by-Feature Breakdown
Document hierarchy and reading-order reconstructionUsually preserves section flow and OCR reading order, but heading semantics are inconsistent.▾
Feature tested: Document hierarchy and reading-order reconstruction
Result: Partial
Verdict: Usually preserves section flow and OCR reading order, but heading semantics are inconsistent.
Expected behavior: Reconstructs page-level structure from both native and scanned PDFs into readable markdown-like text. This was exercised on a Target annual report section ('19. Commitments and Contingencies'), a Sumitomo financial report page with section/subsection headings, a scanned two-column research page headed 'STUDY AREA', and a full annual-report page with a portrait and bullet list.
Test case: Image → Image
Input type: Image
Input used: Input artifact (Image): Target annual report section titled '19. Commitments and Contingencies' with the 'Data Breach' subsection. — landing-ai-target-annual-report-commitments-contingencies-data-breach.png
Observed output: Output artifact (Image): Landing AI preserved the section heading, subsection title, and the full body paragraphs from the Target annual report section. In this example, the reading flo — landing-ai-parsed-commitments-contingencies-data-breach.png
Input artifact: Input artifact (Image): Target annual report section titled '19. Commitments and Contingencies' with the 'Data Breach' subsection. — landing-ai-target-annual-report-commitments-contingencies-data-breach.png
Output artifact: Output artifact (Image): Landing AI preserved the section heading, subsection title, and the full body paragraphs from the Target annual report section. In this example, the reading flo — landing-ai-parsed-commitments-contingencies-data-breach.png
What changed: Image transformed into Image
Test case: Image → Image
Input type: Image
Input used: Input artifact (Image): Sumitomo financial report page titled 'I. Summary of Operating Performance'. — landing-ai-financial-report-operating-performance-page.png
Observed output: Output artifact (Image): On the table-heavy financial report, Landing AI kept the main section title, the subsection title, and the body paragraphs in readable order. The extracted outp — landing-ai-financial-report-operating-performance-parsed-hierarchy.png
Input artifact: Input artifact (Image): Sumitomo financial report page titled 'I. Summary of Operating Performance'. — landing-ai-financial-report-operating-performance-page.png
Output artifact: Output artifact (Image): On the table-heavy financial report, Landing AI kept the main section title, the subsection title, and the body paragraphs in readable order. The extracted outp — landing-ai-financial-report-operating-performance-parsed-hierarchy.png
What changed: Image transformed into Image
Test case: Image → Image
Input type: Image
Input used: Input artifact (Image): Scanned two-column research page with 'STUDY AREA' and 'STAND PRESCRIPTIONS' sections. — landing-ai-scanned-two-column-text-study-area.png
Observed output: Output artifact (Image): On the scanned multi-column page, Landing AI OCR'd the 'STUDY AREA' section into a coherent block of text and kept related paragraphs grouped under that heading — landing-ai-hierarchy-preserved-forest-study-area-text.png
Input artifact: Input artifact (Image): Scanned two-column research page with 'STUDY AREA' and 'STAND PRESCRIPTIONS' sections. — landing-ai-scanned-two-column-text-study-area.png
Output artifact: Output artifact (Image): On the scanned multi-column page, Landing AI OCR'd the 'STUDY AREA' section into a coherent block of text and kept related paragraphs grouped under that heading — landing-ai-hierarchy-preserved-forest-study-area-text.png
What changed: Image transformed into Image
Test case: Image → Image
Input type: Image
Input used: Input artifact (Image): Target annual report page titled 'A Growth Story Again' with a portrait and bullet points. — landing-ai-target-annual-report-growth-story-page.png
Observed output: Output artifact (Image): Landing AI preserved the page title, the main paragraph, the bullet list, and even added a description of the portrait image. The failure is semantic rather tha — landing-ai-target-annual-report-growth-story-parsed-hierarchy.png
Input artifact: Input artifact (Image): Target annual report page titled 'A Growth Story Again' with a portrait and bullet points. — landing-ai-target-annual-report-growth-story-page.png
Output artifact: Output artifact (Image): Landing AI preserved the page title, the main paragraph, the bullet list, and even added a description of the portrait image. The failure is semantic rather tha — landing-ai-target-annual-report-growth-story-parsed-hierarchy.png
What changed: Image transformed into Image
Why it matters / Conclusion: Good at keeping pages readable and in order across mixed PDFs, but not dependable if your downstream pipeline relies on exact heading levels.
Reconstructs page-level structure from both native and scanned PDFs into readable markdown-like text. This was exercised on a Target annual report section ('19. Commitments and Contingencies'), a Sumitomo financial report page with section/subsection headings, a scanned two-column research page headed 'STUDY AREA', and a full annual-report page with a portrait and bullet list.
Target annual report section titled '19. Commitments and Contingencies' with the 'Data Breach' subsection.
Landing AI preserved the section heading, subsection title, and the full body paragraphs from the Target annual report section. In this example, the reading flow stayed intact: the 'Data Breach' heading remained attached to the surrounding narrative, including the payment-card, consumer class action, and financial institutions class action paragraphs.
Sumitomo financial report page titled 'I. Summary of Operating Performance'.
On the table-heavy financial report, Landing AI kept the main section title, the subsection title, and the body paragraphs in readable order. The extracted output separates the summary heading from the paragraph text clearly enough to follow the original section organization.
Scanned two-column research page with 'STUDY AREA' and 'STAND PRESCRIPTIONS' sections.
On the scanned multi-column page, Landing AI OCR'd the 'STUDY AREA' section into a coherent block of text and kept related paragraphs grouped under that heading rather than interleaving the columns. The extraction is readable, though not letter-perfect: for example, one sentence shifts from 'growth factor during the season' to 'growth factor for the season,' and some punctuation/hyphenation is normalized.
Target annual report page titled 'A Growth Story Again' with a portrait and bullet points.
Landing AI preserved the page title, the main paragraph, the bullet list, and even added a description of the portrait image. The failure is semantic rather than textual: the top-level heading is rendered as plain text instead of a true H1-style markdown heading, so the content survives but the hierarchy is weaker than the source.
Table reconstructionStrong on clean financial tables, weaker on nested header semantics and noisier scanned tables.▾
Feature tested: Table reconstruction
Result: Partial
Verdict: Strong on clean financial tables, weaker on nested header semantics and noisier scanned tables.
Expected behavior: Extracts tables into structured markdown-like layouts that usually preserve rows, columns, and values. This was tested on the Target 'Financial Summary' table, a Sumitomo segment comparison table, a more complex multi-level segment table, and a photographed stand-structure table from the scanned research paper.
Test case: Image → Image
Input type: Image
Input used: Input artifact (Image): Target annual report 'Financial Summary' table for 2015 through 2011. — landing-ai-target-annual-report-financial-summary-table-2.png
Observed output: Output artifact (Image): Landing AI reconstructed the Target financial summary with the year columns, row labels, and corresponding values still aligned. In this clean digital table, th — landing-ai-landingai-hybrid-earnings-pdf-parsed-table.png
Input artifact: Input artifact (Image): Target annual report 'Financial Summary' table for 2015 through 2011. — landing-ai-target-annual-report-financial-summary-table-2.png
Output artifact: Output artifact (Image): Landing AI reconstructed the Target financial summary with the year columns, row labels, and corresponding values still aligned. In this clean digital table, th — landing-ai-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 results table comparing previous first quarter, present first quarter, and Y/Y change. — landing-ai-segment-results-table-2025-first-quarter.png
Observed output: Output artifact (Image): Landing AI preserved the segment table's core structure and values: business-segment rows such as Mechatronics, Industrial Machinery, Logistics & Construction, — landing-ai-segment-quarter-yoy-change-table.png
Input artifact: Input artifact (Image): Segment results table comparing previous first quarter, present first quarter, and Y/Y change. — landing-ai-segment-results-table-2025-first-quarter.png
Output artifact: Output artifact (Image): Landing AI preserved the segment table's core structure and values: business-segment rows such as Mechatronics, Industrial Machinery, Logistics & Construction, — landing-ai-segment-quarter-yoy-change-table.png
What changed: Image transformed into Image
Test case: Image → Image
Input type: Image
Input used: Input artifact (Image): Multi-level financial table with segment columns A through F3 and nested headers. — landing-ai-complex-financial-segment-table.png
Observed output: Output artifact (Image): Landing AI kept the values and the broad tabular shape of the complex segment table, but it flattened nested header relationships. The source distinguishes mult — landing-ai-parsed-multilevel-segment-table.png
Input artifact: Input artifact (Image): Multi-level financial table with segment columns A through F3 and nested headers. — landing-ai-complex-financial-segment-table.png
Output artifact: Output artifact (Image): Landing AI kept the values and the broad tabular shape of the complex segment table, but it flattened nested header relationships. The source distinguishes mult — landing-ai-parsed-multilevel-segment-table.png
What changed: Image transformed into Image
Test case: Image → Image
Input type: Image
Input used: Input artifact (Image): Photographed table titled 'Table 2.—Stand structure before and after cutting'. — landing-ai-stand-structure-before-after-cutting-table-2.png
Observed output: Output artifact (Image): On the scanned photographed table, Landing AI preserved the wide table layout and most row/column labels, but OCR introduced real numeric mistakes. Compared wit — landing-ai-lodgepole-pine-diameter-class-table.png
Input artifact: Input artifact (Image): Photographed table titled 'Table 2.—Stand structure before and after cutting'. — landing-ai-stand-structure-before-after-cutting-table-2.png
Output artifact: Output artifact (Image): On the scanned photographed table, Landing AI preserved the wide table layout and most row/column labels, but OCR introduced real numeric mistakes. Compared wit — landing-ai-lodgepole-pine-diameter-class-table.png
What changed: Image transformed into Image
Why it matters / Conclusion: A strong choice for born-digital financial tables, but scanned or photographed tables still need QA, especially when numeric accuracy matters.
Extracts tables into structured markdown-like layouts that usually preserve rows, columns, and values. This was tested on the Target 'Financial Summary' table, a Sumitomo segment comparison table, a more complex multi-level segment table, and a photographed stand-structure table from the scanned research paper.
Target annual report 'Financial Summary' table for 2015 through 2011.
Landing AI reconstructed the Target financial summary with the year columns, row labels, and corresponding values still aligned. In this clean digital table, the extracted layout remained easy to read, including rows like Sales, Cost of sales, SG&A, EBIT, and Net earnings/(loss).
Segment results table comparing previous first quarter, present first quarter, and Y/Y change.
Landing AI preserved the segment table's core structure and values: business-segment rows such as Mechatronics, Industrial Machinery, Logistics & Construction, Energy & Lifelines, Others, and Total stayed aligned with prior-quarter, present-quarter, and Y/Y change columns. For this table, the output closely mirrors the original layout.
Multi-level financial table with segment columns A through F3 and nested headers.
Landing AI kept the values and the broad tabular shape of the complex segment table, but it flattened nested header relationships. The source distinguishes multiple header levels such as Item, Segment, A-D, Subtotal, Other, Total, and adjustment columns; the parsed version compresses these into a simpler linear header row, which makes the data readable but weakens the semantic relationships between header levels.
Photographed table titled 'Table 2.—Stand structure before and after cutting'.
On the scanned photographed table, Landing AI preserved the wide table layout and most row/column labels, but OCR introduced real numeric mistakes. Compared with the source, the '12-inch cut' row shows 'Trees cut per acre' as 7.0 instead of 114.0, the 'Clearcut' after-cut row shows a stray 9 where the source shows 0, and the 'Check area' before-cut values are distorted, with 255.0 appearing where the source shows 55.0 in that position. This makes the table inspectable, but not trustworthy without verification.
Chart-to-text conversionConsistently converts charts into detailed textual representations instead of dropping them.▾
Feature tested: Chart-to-text conversion
Result: Passed
Verdict: Consistently converts charts into detailed textual representations instead of dropping them.
Expected behavior: Transforms charts into descriptive text blocks that retain titles, series/category labels, approximate values, and trend direction. This was tested on a SG&A waterfall chart from the hybrid earnings report and a scanned bar chart showing tree mortality by year and cut treatment.
Test case: Image → Image
Input type: Image
Input used: Input artifact (Image): Waterfall chart titled 'Selling, General and Administrative Expense Rate'. — landing-ai-sg-and-a-expense-rate-waterfall-chart-1.png
Observed output: Output artifact (Image): Instead of keeping the original waterfall image, Landing AI translated it into a text summary that retained the chart title, year anchors, category labels, and — landing-ai-parsed-sg-and-a-expense-rate-summary.png
Input artifact: Input artifact (Image): Waterfall chart titled 'Selling, General and Administrative Expense Rate'. — landing-ai-sg-and-a-expense-rate-waterfall-chart-1.png
Output artifact: Output artifact (Image): Instead of keeping the original waterfall image, Landing AI translated it into a text summary that retained the chart title, year anchors, category labels, and — landing-ai-parsed-sg-and-a-expense-rate-summary.png
What changed: Image transformed into Image
Test case: Image → Image
Input type: Image
Input used: Input artifact (Image): Scanned bar chart of tree mortality by year and cut treatment. — landing-ai-tree-mortality-by-year-and-cut-bar-chart-1.png
Observed output: Output artifact (Image): Landing AI converted the scanned bar chart into a structured text description with the legend, year groupings, approximate per-series values, and the vertical d — landing-ai-parsed-tree-mortality-by-year-and-cut-chart.png
Input artifact: Input artifact (Image): Scanned bar chart of tree mortality by year and cut treatment. — landing-ai-tree-mortality-by-year-and-cut-bar-chart-1.png
Output artifact: Output artifact (Image): Landing AI converted the scanned bar chart into a structured text description with the legend, year groupings, approximate per-series values, and the vertical d — landing-ai-parsed-tree-mortality-by-year-and-cut-chart.png
What changed: Image transformed into Image
Why it matters / Conclusion: If your priority is keeping chart information in the markdown rather than preserving the original visual, this is one of Landing AI's clearest strengths in the report.
Transforms charts into descriptive text blocks that retain titles, series/category labels, approximate values, and trend direction. This was tested on a SG&A waterfall chart from the hybrid earnings report and a scanned bar chart showing tree mortality by year and cut treatment.
Waterfall chart titled 'Selling, General and Administrative Expense Rate'.
Instead of keeping the original waterfall image, Landing AI translated it into a text summary that retained the chart title, year anchors, category labels, and the increase/decrease direction for each step. The output captures specific values such as 2013 SG&A Rate 20.2%, Cost Saving Initiatives (0.8)%, Technology 0.2%, Other 0.4%, 2014 SG&A Rate 20.0%, and 2015 SG&A Rate 19.6%.
Scanned bar chart of tree mortality by year and cut treatment.
Landing AI converted the scanned bar chart into a structured text description with the legend, year groupings, approximate per-series values, and the vertical dashed 'CUT COMPLETED' marker. The output preserves the key analytical content, such as the high 1980 value for the check area and the treatment-by-treatment comparisons across 1979, 1980, and 1981.
Signature and attestation region extractionCaptures signature regions as semantic attestations rather than dropping them.▾
Feature tested: Signature and attestation region extraction
Result: Passed
Verdict: Captures signature regions as semantic attestations rather than dropping them.
Expected behavior: Represents signature-heavy document regions as attestation-style elements that preserve the presence, role, and apparent legibility of signatures. This was exercised on the signatures page from the Target annual report.
Test case: Image → Image
Input type: Image
Input used: Input artifact (Image): Target annual report signatures page. — landing-ai-target-annual-report-signatures-page-2.png
Observed output: Output artifact (Image): Landing AI did more than extract nearby text from the signature page: it emitted attestation blocks describing the signature regions themselves. In this example — landing-ai-target-earnings-signatures-parsed-text.png
Input artifact: Input artifact (Image): Target annual report signatures page. — landing-ai-target-annual-report-signatures-page-2.png
Output artifact: Output artifact (Image): Landing AI did more than extract nearby text from the signature page: it emitted attestation blocks describing the signature regions themselves. In this example — landing-ai-target-earnings-signatures-parsed-text.png
What changed: Image transformed into Image
Why it matters / Conclusion: Useful for compliance-style documents where the existence of a signature block matters, even if you do not need handwriting recognition.
Represents signature-heavy document regions as attestation-style elements that preserve the presence, role, and apparent legibility of signatures. This was exercised on the signatures page from the Target annual report.
Target annual report signatures page.
Landing AI did more than extract nearby text from the signature page: it emitted attestation blocks describing the signature regions themselves. In this example, it preserved the Target Corporation attestation for Catherine R. Smith and a second attestation for Brian C. Cornell, including whether the signature appeared legible or illegible and where it sat relative to the signature line and printed title.
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