Adobe PDF Extract API
A strong PDF-to-markdown API for visuals and standard tables, but inconsistent on deeper structure and handwriting.
Great visual fidelity, mixed structural trust
Adobe PDF Extract API performed well when the goal was to keep charts, images, and most standard financial tables intact inside markdown-friendly output. In this research it also OCR'd a scanned paper and preserved some scanned tables, but structural fidelity was uneven: handwritten signatures were not recovered, nested table-of-contents structure flattened, scanned-page hierarchy degraded into dense text blocks, and the tested web workflow required splitting a scanned PDF above 1 MB. It looks strongest when visual retention matters more than perfect semantic structure.
In-Depth Review
Our detailed analysis of Adobe PDF Extract API — features, performance, and real-world testing.
Feature-by-Feature Breakdown
Inline visual retentionAdobe consistently kept charts and embedded visuals in the document flow instead of dropping them.▾
Feature tested: Inline visual retention
Result: Passed
Verdict: Adobe consistently kept charts and embedded visuals in the document flow instead of dropping them.
Expected behavior: Preserves charts, images, and other visual regions as part of the extracted document rather than stripping them out. This was exercised on the 84-page hybrid Target annual report, which included a financial-highlights panel with charts and a portrait image, and on the scanned research paper, where a chart remained positioned under extracted tabular text.
Test case: PDF document → Image
Input type: PDF document
Input used: Input artifact (PDF document): 84-page hybrid earnings report containing native text, financial tables, charts, embedded images, and a scanned signature page. — llamaparse-hybrid-earnings-pdf-1.pdf
Observed output: Output artifact (Image): On the hybrid earnings report, Adobe kept the financial-highlights panel, charts, and portrait image together inside the extracted layout instead of discarding — adobe-pdf-extract-api-target-annual-report-financial-highlights-and-segment-sales.png
Input artifact: Input artifact (PDF document): 84-page hybrid earnings report containing native text, financial tables, charts, embedded images, and a scanned signature page. — llamaparse-hybrid-earnings-pdf-1.pdf
Output artifact: Output artifact (Image): On the hybrid earnings report, Adobe kept the financial-highlights panel, charts, and portrait image together inside the extracted layout instead of discarding — adobe-pdf-extract-api-target-annual-report-financial-highlights-and-segment-sales.png
What changed: PDF document transformed into Image
Test case: PDF document → Image
Input type: PDF document
Input used: Input artifact (PDF document): Second half of the scanned research paper used to test OCR and visual retention on scanned pages. — adobe-pdf-extract-api-scanned-pdf-7-14.pdf
Observed output: Output artifact (Image): On the scanned research paper, Adobe preserved an embedded chart below extracted text, maintaining a continuous reading flow rather than returning text-only out — adobe-pdf-extract-api-parsed-document-with-residual-basal-area-chart.png
Input artifact: Input artifact (PDF document): Second half of the scanned research paper used to test OCR and visual retention on scanned pages. — adobe-pdf-extract-api-scanned-pdf-7-14.pdf
Output artifact: Output artifact (Image): On the scanned research paper, Adobe preserved an embedded chart below extracted text, maintaining a continuous reading flow rather than returning text-only out — adobe-pdf-extract-api-parsed-document-with-residual-basal-area-chart.png
What changed: PDF document transformed into Image
Why it matters / Conclusion: If you need markdown output that still reflects where charts and images appeared in the original PDF, Adobe was reliably good in this test set.
Preserves charts, images, and other visual regions as part of the extracted document rather than stripping them out. This was exercised on the 84-page hybrid Target annual report, which included a financial-highlights panel with charts and a portrait image, and on the scanned research paper, where a chart remained positioned under extracted tabular text.
On the hybrid earnings report, Adobe kept the financial-highlights panel, charts, and portrait image together inside the extracted layout instead of discarding the visuals or moving them out of reading order.
On the scanned research paper, Adobe preserved an embedded chart below extracted text, maintaining a continuous reading flow rather than returning text-only output.
Structured table reconstructionAdobe reconstructed most standard and grouped-column tables cleanly across both digital and scanned inputs.▾
Feature tested: Structured table reconstruction
Result: Passed
Verdict: Adobe reconstructed most standard and grouped-column tables cleanly across both digital and scanned inputs.
Expected behavior: Rebuilds readable tables from PDFs while keeping row labels, columns, and most grouped headers intact. The researcher exercised this on the Target annual report's financial summary table, a quarterly consolidated balance sheet, a grouped-column segment comparison table, and a photographed scanned table from the research paper.
Test case: Image → Image
Input type: Image
Input used: Input artifact (Image): Source financial summary table from the Target annual report. — landing-ai-target-annual-report-financial-summary-table-2.png
Observed output: Output artifact (Image): On the Target financial summary table, Adobe preserved the 2015-2011 columns, the major financial-result row labels, and the value alignment closely enough that — adobe-pdf-extract-api-target-financial-summary-table-dark-background.png
Input artifact: Input artifact (Image): Source financial summary table from the Target annual report. — landing-ai-target-annual-report-financial-summary-table-2.png
Output artifact: Output artifact (Image): On the Target financial summary table, Adobe preserved the 2015-2011 columns, the major financial-result row labels, and the value alignment closely enough that — adobe-pdf-extract-api-target-financial-summary-table-dark-background.png
What changed: Image transformed into Image
Test case: Image → Image
Input type: Image
Input used: Input artifact (Image): Source quarterly consolidated balance sheet from the table-heavy financial report. — adobe-pdf-extract-api-quarterly-consolidated-balance-sheet-scanned-table.png
Observed output: Output artifact (Image): On the quarterly balance sheet, Adobe kept the two date columns, the asset hierarchy, and the numeric values in a clean text-first reconstruction. — adobe-pdf-extract-api-parsed-quarterly-consolidated-balance-sheet.png
Input artifact: Input artifact (Image): Source quarterly consolidated balance sheet from the table-heavy financial report. — adobe-pdf-extract-api-quarterly-consolidated-balance-sheet-scanned-table.png
Output artifact: Output artifact (Image): On the quarterly balance sheet, Adobe kept the two date columns, the asset hierarchy, and the numeric values in a clean text-first reconstruction. — adobe-pdf-extract-api-parsed-quarterly-consolidated-balance-sheet.png
What changed: Image transformed into Image
Test case: Image → Image
Input type: Image
Input used: Input artifact (Image): Source segment comparison table with grouped date-range headers and year-over-year columns. — landing-ai-segment-results-table-2025-first-quarter.png
Observed output: Output artifact (Image): On the grouped-column segment table, Adobe retained the relationship between the date-range headers and their values, preserving a readable comparison across pr — adobe-pdf-extract-api-parsed-segment-comparison-table-1.png
Input artifact: Input artifact (Image): Source segment comparison table with grouped date-range headers and year-over-year columns. — landing-ai-segment-results-table-2025-first-quarter.png
Output artifact: Output artifact (Image): On the grouped-column segment table, Adobe retained the relationship between the date-range headers and their values, preserving a readable comparison across pr — adobe-pdf-extract-api-parsed-segment-comparison-table-1.png
What changed: Image transformed into Image
Test case: Image → Image
Input type: Image
Input used: Input artifact (Image): Photographed scanned table comparing original diameter and diameter after harvest. — mistral-ai-scanned-treatment-diameter-table.png
Observed output: Output artifact (Image): On the scanned diameter table, Adobe reconstructed the rows and measurement columns well enough to stay readable, though OCR introduced a header typo by renderi — adobe-pdf-extract-api-parsed-table-original-diameter-after-harvest.png
Input artifact: Input artifact (Image): Photographed scanned table comparing original diameter and diameter after harvest. — mistral-ai-scanned-treatment-diameter-table.png
Output artifact: Output artifact (Image): On the scanned diameter table, Adobe reconstructed the rows and measurement columns well enough to stay readable, though OCR introduced a header typo by renderi — adobe-pdf-extract-api-parsed-table-original-diameter-after-harvest.png
What changed: Image transformed into Image
Why it matters / Conclusion: Adobe was strongest on ordinary financial tables, grouped-column tables, and at least one clean scanned table, making it a solid option when table readability is the main requirement.
Rebuilds readable tables from PDFs while keeping row labels, columns, and most grouped headers intact. The researcher exercised this on the Target annual report's financial summary table, a quarterly consolidated balance sheet, a grouped-column segment comparison table, and a photographed scanned table from the research paper.
Source financial summary table from the Target annual report.
On the Target financial summary table, Adobe preserved the 2015-2011 columns, the major financial-result row labels, and the value alignment closely enough that the table remained easy to read rather than collapsing into plain text.
Source quarterly consolidated balance sheet from the table-heavy financial report.
On the quarterly balance sheet, Adobe kept the two date columns, the asset hierarchy, and the numeric values in a clean text-first reconstruction.
Source segment comparison table with grouped date-range headers and year-over-year columns.
On the grouped-column segment table, Adobe retained the relationship between the date-range headers and their values, preserving a readable comparison across previous quarter, present quarter, and year-over-year change.
Photographed scanned table comparing original diameter and diameter after harvest.
On the scanned diameter table, Adobe reconstructed the rows and measurement columns well enough to stay readable, though OCR introduced a header typo by rendering 'after harvest' as 'alter harvest'.
Document structure and hierarchy preservationTop-level structure survived well on clean digital pages, but nested hierarchy and scanned-page organization were inconsistent.▾
Feature tested: Document structure and hierarchy preservation
Result: Partial
Verdict: Top-level structure survived well on clean digital pages, but nested hierarchy and scanned-page organization were inconsistent.
Expected behavior: Extracts headings, sections, and reading order into markdown-oriented output. The researcher tested this on a native-digital operating-performance page, a financial-report table of contents, and the opening page of a scanned research paper. The scanned-paper workflow also exposed a tested web-interface upload limit that forced document splitting.
Test case: Image → Image
Input type: Image
Input used: Input artifact (Image): Source operating-performance page from the financial report. — adobe-pdf-extract-api-summary-of-operating-performance-report-page.png
Observed output: Output artifact (Image): On a clean digital report page, Adobe preserved the main section title, subsection title, and paragraph reading order, showing good document-level structure on — adobe-pdf-extract-api-operating-performance-hierarchy-view.png
Input artifact: Input artifact (Image): Source operating-performance page from the financial report. — adobe-pdf-extract-api-summary-of-operating-performance-report-page.png
Output artifact: Output artifact (Image): On a clean digital report page, Adobe preserved the main section title, subsection title, and paragraph reading order, showing good document-level structure on — adobe-pdf-extract-api-operating-performance-hierarchy-view.png
What changed: Image transformed into Image
Test case: Image → Image
Input type: Image
Input used: Input artifact (Image): Table of contents page from the 18-page financial report. — financialpdf_toc-1.png
Observed output: Output artifact (Image): On the table of contents, Adobe flattened nested entries into a mostly linear list, so indentation-based relationships between sections and subsections were no — adobe-pdf-extract-api-supplementary-materials-table-of-contents-2.png
Input artifact: Input artifact (Image): Table of contents page from the 18-page financial report. — financialpdf_toc-1.png
Output artifact: Output artifact (Image): On the table of contents, Adobe flattened nested entries into a mostly linear list, so indentation-based relationships between sections and subsections were no — adobe-pdf-extract-api-supplementary-materials-table-of-contents-2.png
What changed: Image transformed into Image
Test case: Image → Image
Input type: Image
Input used: Input artifact (Image): Opening title, abstract, and keyword page from the scanned research paper. — scanned_pdf_page_1.png
Observed output: Output artifact (Image): On the scanned research paper's opening page, Adobe OCR'd the content but returned the title, abstract, keywords, and opening prose as a dense block with minima — adobe-pdf-extract-api-usda-research-note-ocr-text.png
Input artifact: Input artifact (Image): Opening title, abstract, and keyword page from the scanned research paper. — scanned_pdf_page_1.png
Output artifact: Output artifact (Image): On the scanned research paper's opening page, Adobe OCR'd the content but returned the title, abstract, keywords, and opening prose as a dense block with minima — adobe-pdf-extract-api-usda-research-note-ocr-text.png
What changed: Image transformed into Image
Why it matters / Conclusion: Adobe preserves top-level structure on clean digital pages, but it is less dependable when hierarchy is nested, scanned, or spread across longer files in the tested web workflow.
Extracts headings, sections, and reading order into markdown-oriented output. The researcher tested this on a native-digital operating-performance page, a financial-report table of contents, and the opening page of a scanned research paper. The scanned-paper workflow also exposed a tested web-interface upload limit that forced document splitting.
Source operating-performance page from the financial report.
On a clean digital report page, Adobe preserved the main section title, subsection title, and paragraph reading order, showing good document-level structure on native text content.
Table of contents page from the 18-page financial report.
On the table of contents, Adobe flattened nested entries into a mostly linear list, so indentation-based relationships between sections and subsections were no longer clearly preserved.
Opening title, abstract, and keyword page from the scanned research paper.
On the scanned research paper's opening page, Adobe OCR'd the content but returned the title, abstract, keywords, and opening prose as a dense block with minimal structural separation, which reduced the usefulness of hierarchy cues.
Advanced OCR and semantic layout handlingAdobe handled printed text much better than handwriting or multi-role header semantics.▾
Feature tested: Advanced OCR and semantic layout handling
Result: Failed
Verdict: Adobe handled printed text much better than handwriting or multi-role header semantics.
Expected behavior: Attempts to recover difficult content beyond straightforward printed text and standard grids. The research exercised this on a scanned Target signatures page containing handwritten signatures and on a complex financial table with dual header roles and multiple summary columns.
Test case: Image → Image
Input type: Image
Input used: Input artifact (Image): Source signatures page from the Target annual report with printed legal text and handwritten signatures. — adobe-pdf-extract-api-target-annual-report-signatures-page-1.png
Observed output: Output artifact (Image): On the signatures page, Adobe captured the printed legal text, dates, and typed names, but it did not recover the handwritten signatures themselves. — adobe-pdf-extract-api-target-annual-report-signatures-ocr-text.png
Input artifact: Input artifact (Image): Source signatures page from the Target annual report with printed legal text and handwritten signatures. — adobe-pdf-extract-api-target-annual-report-signatures-page-1.png
Output artifact: Output artifact (Image): On the signatures page, Adobe captured the printed legal text, dates, and typed names, but it did not recover the handwritten signatures themselves. — adobe-pdf-extract-api-target-annual-report-signatures-ocr-text.png
What changed: Image transformed into Image
Test case: Image → Image
Input type: Image
Input used: Input artifact (Image): Source complex segment table with dual header roles and multiple grouped columns. — landing-ai-complex-financial-segment-table.png
Observed output: Output artifact (Image): On the complex segment table, Adobe flattened the multi-header structure into a single-line header pattern, which blurred the distinction between row headers, c — adobe-pdf-extract-api-parsed-multiheader-segment-table-2.png
Input artifact: Input artifact (Image): Source complex segment table with dual header roles and multiple grouped columns. — landing-ai-complex-financial-segment-table.png
Output artifact: Output artifact (Image): On the complex segment table, Adobe flattened the multi-header structure into a single-line header pattern, which blurred the distinction between row headers, c — adobe-pdf-extract-api-parsed-multiheader-segment-table-2.png
What changed: Image transformed into Image
Why it matters / Conclusion: Adobe is not a good fit if your PDFs rely on handwriting recognition or on nuanced table semantics that must stay perfectly explicit in markdown.
Attempts to recover difficult content beyond straightforward printed text and standard grids. The research exercised this on a scanned Target signatures page containing handwritten signatures and on a complex financial table with dual header roles and multiple summary columns.
Source signatures page from the Target annual report with printed legal text and handwritten signatures.
On the signatures page, Adobe captured the printed legal text, dates, and typed names, but it did not recover the handwritten signatures themselves.
Source complex segment table with dual header roles and multiple grouped columns.
On the complex segment table, Adobe flattened the multi-header structure into a single-line header pattern, which blurred the distinction between row headers, column headers, subtotal columns, and adjustment columns.
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