Best AI Tools to Convert Complex PDFs into Clean Markdown with an API

On the Target annual report's 'A Growth Story Again' page, Extend AI linearized the two-column layout into readable markdown while keeping the page title, narrative text, and bullet-style business highlights together.

For the 'Financial Summary' table, Extend AI preserved the 2015 to 2011 year columns and kept rows such as Sales, Cost of sales, SG&A, EBIT, and net earnings aligned with their values.

Extend AI converted the SG&A waterfall chart into a structured figure block that kept the step values, category labels, and a prose summary of the movement from 20.2% in 2013 to 19.6% in 2015.

The CEO signature block was recovered as readable text, preserving Brian C. Cornell's name, title, and date instead of skipping the scanned block.

From the blurred audit stamp, Extend AI recovered the firm name and page number, but misread 'LLP' as '1LP'.

The Target logo was not embedded inline, but Extend AI preserved it as a structured figure description identifying the bullseye symbol.

On the Sumitomo 'Additional Notes' page, Extend AI kept the section heading and numbered subpoints in order, although the visible screenshot cuts off the last share-count lines.

In the segment comparison table, Extend AI preserved grouped columns for previous quarter, present quarter, and year-over-year change with values aligned by segment.

For a denser multilevel segment table, Extend AI kept most values but merged compound header roles, making parent and child header distinctions less explicit.

From the scanned two-column research page, Extend AI reconstructed a readable section flow under 'STUDY AREA' and kept successive paragraphs attached to the heading.

Extend AI transcribed the photographed stand-structure table into a readable row-column layout, including before-cut and after-cut groupings and treatment rows.

For the mortality chart, Extend AI preserved legend labels, years, and the 'CUT COMPLETED' marker inside a figure caption, but replaced the original visual trend shape with text.

The scanned cover's faint pencil marks were not truly deciphered, but Extend AI at least surfaced them as a figure with a note that most of the cursive text was illegible.

In a dense multirow scanned table, Extend AI recovered many values but the year and header relationships broke down, so the grouped structure became hard to follow.

When rotated text interrupted a scanned table, Extend AI reconstructed most rows but lost the between-column annotation and introduced corrupted cells such as extra characters in numeric positions.

LlamaParse preserved the Target report's section hierarchy and content flow instead of flattening the multi-column page into disconnected text fragments.

Rather than embed the SG&A chart as an image, LlamaParse translated it into structured textual data so the chart's categories and values remained machine-usable in markdown.

The blurred Ernst & Young marking remained recognizable in LlamaParse's output instead of disappearing during OCR.

The annual report's financial summary remained readable with row alignment, year columns, and value associations preserved.

LlamaParse described document visuals in text instead of dropping them, showing how non-text assets were surfaced inside the extracted content.

Signature content was retained as descriptive text rather than as an embedded handwritten image, preserving document meaning even when the exact strokes were not reproduced.

LlamaParse exposed visual assets as separate downloadable outputs rather than only inside the markdown file.

On the financial report, LlamaParse preserved the document title, section headings, and overall content flow well enough that the original hierarchy remained recognizable.

For one grouped-column financial table, LlamaParse preserved the multi-level column organization well enough that header-to-data relationships were still understandable.

In a harder financial table, visible values survived but parent-child header roles became less explicit, weakening the semantic clarity of grouped columns.

The table of contents was extracted as sequential text with page numbers instead of as a structurally nested navigation block.

LlamaParse reconstructed the scanned research paper into a coherent reading flow, keeping headings attached to the paragraphs that followed across the multi-column layout.

The overall structure of a scanned grouped-column table remained readable, with much of the column organization and associated data preserved.

The scanned chart was converted into structured table-like content, keeping legend-to-value mapping even though the original chart image was not preserved inline.

In the hardest scanned multilevel table, grouped header relationships became ambiguous even though much of the table content was still recoverable.

Landing AI preserved page flow and document hierarchy in the commitments section, keeping headings attached to their associated narrative content.

The annual report's financial table was reconstructed with its overall layout intact, preserving header, row, and value relationships well enough to remain readable.

Instead of embedding the SG&A waterfall chart, Landing AI converted it into text that preserved the step values, category relationships, and direction of change.

Landing AI preserved the presence of the signature region through an attestation-style element rather than reproducing the handwritten signatures themselves.

The document's highest-level heading was flattened into plain text rather than being preserved as a true H1, so the content survived but heading semantics weakened.

On one financial report section, Landing AI kept heading hierarchy and supporting content together well enough that the original organization remained clear.

Landing AI reconstructed a grouped financial table with row alignment and column organization that closely followed the source layout.

In a harder financial table, nested header levels were merged into single cells, weakening the semantic relationships between parent and child headers.

For the scanned research paper, Landing AI reconstructed the multi-column layout into a readable heading-and-paragraph flow.

The scanned chart was converted into descriptive text that reflected its values and legend relationships instead of being silently dropped.

Across much of the annual report, Mistral AI kept headings and supporting content structurally aligned instead of flattening everything into one text stream.

Mistral AI reconstructed the layered financial table into a usable structured layout without losing the core row, column, and value relationships.

Mistral AI exported page-wise outputs into a folder structure, which makes it easier to inspect specific pages during review.

Visual assets such as charts and signatures were preserved as page-associated files rather than disappearing from the extraction.

The blurred audit stamp was still recognized by Mistral AI, showing that low-visibility text was not entirely lost.

In other sections of the annual report, Mistral AI flattened heading hierarchy inconsistently even when the underlying text was recovered.

The financial-report export included both per-page files and a consolidated markdown document, supporting both granular inspection and end-to-end use.

On a narrative financial-report page, headings and associated text were kept in a readable flow.

For one grouped financial table, Mistral AI preserved hierarchical header structure well enough to keep the relationships between headers and columns understandable.

The financial report's table of contents was recovered as flat text rather than as structured nested navigation.

In a harder multilevel table, two header levels were combined into single cells, weakening the table's parent-child column semantics.

On the scanned research paper, Mistral AI generally kept section hierarchy and reading flow intact despite the underlying scan.

A scanned multicolumn table was reconstructed with much of its layout logic still visible.

For the scanned paper, Mistral AI again returned page-wise markdown and associated assets in a foldered output.

Charts from the scanned paper were retained as page-associated visual assets instead of being dropped.

Tensorlake preserved heading order and section relationships well enough that the annual report's structure remained close to the source.

The annual report's financial table stayed structurally faithful, with row, column, and value relationships preserved.

Tensorlake exposed chart data beyond plain markdown text, making the underlying waterfall-chart information more explicit.

Scanned signature content was detected and parsed instead of being skipped entirely, even if it was not always categorized cleanly.

Tensorlake recovered the Ernst & Young reference from the blurred stamp but misrendered the ampersand as a plus sign.

In the tested web workflow, the markdown appeared as copyable content rather than a directly downloadable file.

Even across a table-heavy financial report, Tensorlake kept section ordering and document flow reasonably intact.

One complex financial table was preserved inside the markdown with readable row-column structure and integration into the surrounding document.

In a more difficult multi-header table, Tensorlake missed part of the header hierarchy and dropped at least one header label, making the reconstruction incomplete.

For the scanned research paper, section-level hierarchy remained readable even on multicolumn pages.

Tensorlake converted chart information into tabular data during standard extraction without needing a separate chart-only mode.

On scanned grouped-column tables, Tensorlake misplaced headers and weakened column relationships enough that the table structure became unreliable.

In the hardest scanned table, value positions shifted and header assignments broke down, showing a systemic weakness on scanned multilevel tables.

Adobe kept charts, images, and other visual assets integrated into the output instead of separating or dropping them.

The annual report's financial summary was reconstructed with strong row-column retention that closely matched the source layout.

Adobe preserved the printed signature-page text but omitted the handwritten signatures themselves, leaving the scanned signing marks absent from the result.

On the financial report, Adobe preserved major sections and much of the narrative hierarchy well enough for the page to remain readable.

The balance sheet came through as a clean aligned table, keeping asset rows and comparison columns readable.

Adobe reconstructed grouped financial columns well in the segment-performance table, keeping headers associated with the correct values.

In a harder dual-header table, Adobe flattened row-header and column-header roles together, weakening the table's structural meaning.

The financial report's TOC was flattened into a list rather than preserved as a nested structured contents section.

On one scanned grouped table, Adobe preserved the compact layout and underlying grouped relationships better than many lower-ranked tools.

Charts from the scanned paper were preserved in place as visual assets, contributing to a more continuous reading experience.

When Adobe processed scanned content, section boundaries and title-page organization weakened, leaving content present but less clearly structured.

Upstage preserved the main structure of the annual report's financial table and kept most values in the correct places, although some currency symbols were missed.

The waterfall chart was translated into extracted values and explanatory text rather than being preserved as a directly readable visual.

On the signatures page, section structure collapsed and handwritten signatures were not clearly preserved, even though some nearby printed text remained.

In multicolumn annual-report sections, heading alignment and navigational structure diverged from the source, making the reading flow noticeably weaker.

On a simpler financial-report section, Upstage preserved enough hierarchy that the basic structure of the page remained recognizable.

In a more complex quarterly balance-sheet layout, column headers drifted away from their corresponding data regions, producing a structurally inconsistent table.

Major section headings in the financial report were flattened so they were no longer clearly distinguished from the body text they introduced.

On a scanned grouped-column table, much of the data survived but header reconstruction was weak enough that the table's structure no longer matched the source cleanly.

For the scanned figure, Upstage extracted chart details as raw delimiter-heavy data that preserved some values but not a meaningfully organized chart structure.

On one annual-report section, Nutrient.io preserved the intended reading order and enough structure that headings and surrounding content remained connected.

Even on a relatively standard annual-report financial table, Nutrient.io misaligned significant parts of the layout, weakening row, column, and value relationships.

Nutrient.io preserved signature-related printed text but did not extract the handwritten signature itself, leaving the signed portion incomplete.

Some local content recovery was possible in the financial report, showing that isolated pages could retain a usable section structure.

Paragraph boundaries and narrative flow were not consistently preserved, so content on the title-and-abstract page became fragmented compared with the source.

Nutrient.io could recover parts of disclosure-style narrative content, but that strength did not extend consistently to the whole report.

In a multilevel financial table, Nutrient.io lost parts of the grouped header organization, making parent-child column relationships hard to recover.

Another financial table showed the same weakness: values were present, but the structure did not accurately preserve the original multi-header layout.

On a simpler scanned research section, Nutrient.io kept section headings aligned with their related column content better than it did on harder layouts.

A scanned grouped-column table was partially preserved, showing that Nutrient.io could recover some internal organization on less difficult examples.

For the scanned chart, values surfaced in a linearized form that lost the original chart's grouping and visual relationships, making the result hard to interpret.

As scanned table complexity increased, structural boundaries broke down and cell relationships were misaligned, merged incorrectly, or lost.

On the opening scanned research page, page layout was misread badly enough that structural elements such as title and abstract relationships became unreliable.