Documents arrive in every shape imaginable — a PDF in your inbox, a phone photo of a printout, a scanned form, a screenshot. The Docyield document parser reads all of them. Upload a PDF or an image and the tool returns the content as clean text, or as structured JSON, CSV, or XML when you need fields and tables rather than prose. One general-purpose parser instead of a different workaround for every file type.
What makes a document hard to work with isn't its contents — it's the container. A PDF fixes characters in place; an image has no characters at all, only pixels. Both hide their meaning from software. Docyield reads the embedded text where it exists and runs OCR where it doesn't, then interprets the layout so the output reflects what the document actually says, not just where ink happened to land on the page.
One parser for PDFs and images alike
The document parser accepts both PDFs and image files — PNG, JPG, WEBP, and the like — and treats them as the same problem: recover the readable content in the right order. For a native PDF it reads the text layer directly; for a scan or photo it runs OCR first. Either way you get the document's content back without retyping it.
That breadth is the point. You rarely control the format a document reaches you in, and switching tools per file type is friction nobody wants. A single parser that copes with a crisp PDF and a slightly skewed phone photo alike means one workflow regardless of where the file came from.
Why parsing beats reading by hand or plain OCR
Retyping a document is slow and error-prone, and even copy-paste fails the moment the source is an image or a locked PDF. Raw OCR is a step up, but it stops at characters: it gives you the words without telling you which is a label, which is a value, or where a table's rows begin and end.
The document parser goes the extra step. It preserves reading order across columns, keeps tables as rows and cells, and — when you ask for structured output — maps labelled fields into keys and repeated rows into arrays. You get content you can use directly rather than text you still have to untangle.
Text or structure: choosing what to ask for
The parser can hand back plain prose or structured data, and picking the right one upfront saves rework. Reach for plain text when you mostly care about the words — searching an archive, quoting a passage, summarising a report, or feeding content to a model that reads naturally. The text output is compact, easy to skim, and carries none of the structural overhead you don't need for those jobs.
Reach for structured output — JSON, CSV, or XML — when the document's value is in its fields and rows: a form whose answers you want as named values, a table you intend to sum, a record you'll load into a database. In those cases the structure is the point, and getting it from the parser saves you from rebuilding it by hand afterwards. The same upload yields both, so if you are unsure, start with text and switch tabs to the structured view to see which fits.
Who uses a document parser
- Teams digitising mixed piles of documents — PDFs, scans, and photos — into one consistent format.
- Developers who need a single endpoint that copes with whatever file a user uploads.
- Operations and back-office staff turning paperwork into searchable, editable text.
- Researchers and analysts extracting content from heterogeneous source files.
- Anyone who just needs the words or data out of a document and doesn't care what container it came in.
Accuracy and where it has limits
Results follow the source. Native PDF text is recovered cleanly and faithfully; OCR on a clear, well-lit scan or photo is reliable, while a blurry, low-contrast, or steeply angled image is genuinely harder. Sharper inputs — good focus, even lighting, the page filling the frame — consistently produce better output.
The parser never fabricates content. Where a region is illegible or a value simply isn't present, it returns nothing for that part rather than a confident guess, because a blank is easier to spot and fix than a wrong answer buried in plausible text. For the small share of documents that are awkward or low quality, a quick check against the original is the cheapest safeguard.
Layouts, languages, and tables
Documents come with multiple columns, headers and footers, sidebars, footnotes, and tables sitting alongside prose. A naive extractor reads these in raw position order and produces text that hops between columns mid-sentence. Docyield reconstructs the reading order so multi-column pages come out coherent, and it keeps tabular regions as rows and cells rather than flattening them.
The parser also reads a wide range of languages and scripts, not just English, so documents in mixed or non-Latin languages are handled rather than mangled. Where prose and tables share a page, both are preserved instead of one being lost to the other.
One workflow for mixed file types
The real value of a general parser shows up when your inputs are inconsistent. A single inbox might hold a digital PDF, a scan someone made of the same document, and a photo a colleague snapped on their phone — three containers for what is essentially the same kind of content. Routing each to a different specialised tool is busywork; sending them all to one parser that reads whatever it is given keeps the workflow simple and the output uniform.
That uniformity matters downstream. Because a PDF and an image of the same page come back in the same output shape, the system or spreadsheet that consumes them doesn't need to know or care which format the original was. You standardise once, at the parse step, instead of writing branches for every file type your users might upload. For teams that can't dictate how documents arrive, that single front door is often the whole reason to use a parser at all.
Output formats and growing past one file
Plain text is the default and covers a lot of ground — search, quoting, feeding content into an LLM. The same parse is also available as JSON, CSV, Excel (XLSX), or XML from the result view, so structured fields and rows are one tab away when prose isn't enough. All of them come from one read of the document, so getting a second format never means uploading the file again.
The free parser handles a single file at a time, which suits one-off jobs. When you need to process documents continuously, in bulk, or straight from your own software, the Docyield API and batch dashboard run the same extraction with webhook delivery, so behaviour stays identical as you scale. The dashboard shows each file's status and lets you re-run any that need a second pass, which keeps a steady inflow of mixed documents from turning into a backlog.
How to parse a document
- 1Upload your file — drop a PDF or an image (PNG, JPG, WEBP) onto the box above, or click to choose one.
- 2Wait a few seconds while Docyield reads the text or runs OCR on a scan or photo.
- 3Review the extracted content and check any part against the original.
- 4Choose your output — plain text, or JSON, CSV, Excel, or XML for structured data.
- 5Copy the result or download the file, ready to search, edit, or feed into your tools.
Frequently asked questions
Processing documents at scale?
Batch upload, an extraction API, and webhooks for 100+ documents a month.