DocsGPT/docs/content/Guides/ocr.mdx

---
title: Document Parsing and OCR
description: Which engine parses uploaded documents, how OCR works in DocsGPT, how to configure it, and what changes for source ingestion vs chat attachments.
---

import { Callout } from 'nextra/components'

# Document Parsing and OCR for Sources and Attachments

## Parser engine

DocsGPT converts uploaded documents to Markdown before chunking, embedding or
handing them to a model. The engine is selected with one setting:

```env
DOC_PARSER_ENGINE=anydoc
```

- `anydoc` (default): [firecrawl-anydoc](https://github.com/firecrawl/anydoc),
  a Rust converter with no ML models. It reads PDF, DOCX, PPTX, XLSX and CSV
  in milliseconds with ~100 MB peak memory; HTML/XHTML is converted with
  `markdownify`, head-truncated at `MARKUP_MAX_BYTES`. Source uploads and the
  `read_document` tool accept the same file types as before (the
  `SUPPORTED_SOURCE_EXTENSIONS` whitelist is unchanged); chat attachments,
  which are not whitelisted by extension, can additionally be parsed from
  anydoc's other formats (DOC, PPT/PPS/POT, XLS, ODT/ODS/ODP, RTF and the
  macro-enabled Office variants). It never performs OCR:
  a scanned or image-only PDF is *detected* and handed to the fallback
  parser — the active OCR backend when OCR is on (see below), Docling when
  it is installed, the legacy text parsers otherwise — and if nothing can
  read the file the upload fails with a clear error instead of storing an
  empty document.
- `docling`: the previous default. Docling's layout and table models produce
  structured Markdown, support OCR, and back the `read_document` tool's
  `structured` output, at the cost of a large dependency tree (torch,
  transformers, ONNX models) and seconds to minutes per PDF. Switching back is
  this one variable; nothing else changes.

With `DOC_PARSER_ENGINE=anydoc`, Docling still handles what anydoc cannot when
it is installed: `.adoc`, `.vtt` and `.xml` sources, the fallback for files
anydoc rejects, and — when it is the OCR backend — scanned PDFs and images.
Without Docling those formats use the standard parsers; OCR still works
through the native backend, and with OCR off images are only read when
`PARSE_IMAGE_REMOTE=true`.

## Installing the docling engine

docling is not part of the base install, and OCR does not need it (see
[OCR backends](#ocr-backends)). Add it when you want its layout-model OCR,
`.adoc`/`.vtt`/`.xml` source parsing, or `read_document`'s `structured`
output:

```bash
pip install -r application/requirements-docling.txt
```

Docker images build without it by default; opt in with the build argument:

```bash
docker build --build-arg INSTALL_DOCLING=true ./application
```

`deployment/docker-compose.yaml` forwards the same switch, so setting
`INSTALL_DOCLING=true` in `.env` (or the shell) bakes docling into locally
built backend and worker images; `setup.sh` offers it as a follow-up to the
OCR question. Either way no code changes are needed — docling is picked up as
the fallback engine (and, under `OCR_BACKEND=auto`, as the OCR backend) as
soon as it is importable, and `DOC_PARSER_ENGINE=docling` makes it the
primary parser.

## OCR

OCR is optional and controlled by two on/off settings, a backend and an
engine choice:

```env
OCR_ENABLED=false
OCR_ATTACHMENTS_ENABLED=false
OCR_BACKEND=auto
OCR_ENGINE=tesseract
```

- `OCR_ENABLED`: OCR behavior for Source Docs ingestion.
- `OCR_ATTACHMENTS_ENABLED`: OCR behavior for chat attachments uploaded from the message box.
- `OCR_BACKEND`: which stack performs the OCR (next section).
- `OCR_ENGINE`: which recognition engine it uses ([Choosing the OCR engine](#choosing-the-ocr-engine)).

The older names `DOCLING_OCR_ENABLED` and `DOCLING_OCR_ATTACHMENTS_ENABLED`
are still accepted as aliases.

Under the default anydoc engine, a scanned PDF reaches OCR through anydoc's
own detection: anydoc refuses it ("OCR is required") and the OCR backend
takes over as the fallback parser. If that fallback also extracts almost
nothing — OCR off, or no engine available — the upload fails with a clear
message instead of silently indexing an empty document.

Mixed documents — text pages with scanned pages among them — convert
through anydoc, which reads the text pages and skips the scanned ones. With
OCR on, DocsGPT probes every page's text layer, OCRs the pages that have
none through the active backend, and appends their text; the document's
metadata records the count as `ocr_pages`. With OCR off, only the text pages
are indexed.

## OCR backends

Docling is not the only way to OCR. The `native` backend renders the pages
that need it with pypdfium2 and Pillow — both already core dependencies —
and feeds them straight into tesseract or a DeepSeek-OCR endpoint. No ML
models load in the worker, and nothing beyond the ~35 MB tesseract binary
(shipped in the Docker image) is installed.

| `OCR_BACKEND` | What runs | When to pick it |
|---|---|---|
| `auto` (default) | Docling when the docling extra is installed, `native` otherwise. | Leave it: a plain install gets working OCR from tesseract alone, and installing docling upgrades OCR without touching config. |
| `native` | pypdfium2 + Pillow page rendering into `tesseract` or `deepseek`. Pages that carry a text layer are read directly and never OCR'd; pages without one are rendered at `OCR_RENDER_DPI` (200) and OCR'd. Multi-frame TIFFs are read frame by frame. | You do not want docling's dependency tree or memory footprint, or you have docling installed for `structured` output but want lightweight OCR. |
| `docling` | Docling's layout-model pipeline: hybrid OCR (only the bitmap regions of a page), reading-order recovery, TableFormer table structure, and the `auto` / `ocrmac` / `rapidocr` engines. | Multi-column scans, scanned tables you need as Markdown tables under tesseract, or macOS `ocrmac`. Needs the docling extra. |

The trade-off is the layout model. Under `native`, multi-column scans rely
on tesseract's own page segmentation and tesseract yields tables as plain
lines; DeepSeek-OCR emits Markdown tables itself, so it remains the quality
path on either backend. Under `DOC_PARSER_ENGINE=docling` with the `native`
backend, PDFs whose every page has a text layer still go through Docling
(OCR off) for its structured Markdown; only documents with scanned pages
take the native path.

## Choosing the OCR engine

Benchmarked 2026-08 on English, bilingual EN/ZH, table-heavy and degraded
scans (all engines driven through docling so layout handling is identical):

| `OCR_ENGINE` | Backends | Role | Notes |
|---|---|---|---|
| `tesseract` | native, docling | **recommended default** | Best classic-engine accuracy in the bench: perfect EN word recall on all docs, 0.000 CER on the bilingual page, 100% table cells, robust to mild degradation. ~35 MB of *system* packages, CPU-only. Needs the `tesseract` binary + language packs — shipped in the Docker image (opt out with `--build-arg INSTALL_TESSERACT=false`); set languages via `OCR_LANGS` (e.g. `eng+chi_sim`). |
| `deepseek` | native, docling | best quality, heavy on the *server* | DeepSeek-OCR against an OpenAI-compatible endpoint. Only engine that reconstructs totals rows as table rows; near-perfect CJK; barely affected by degradation. The ingestion worker stays light (no layout models); the model runs in Ollama or vLLM. Costs: a GPU/Apple-Silicon endpoint, ~seconds per page, and occasional silent drops of page-level elements (titles). |
| `auto` | docling | convenience | docling picks: `ocrmac` on macOS (excellent, ~1 s/page), `rapidocr` on Linux — see below before relying on it server-side. Also docling's automatic fallback whenever the selected engine is not installed. The native backend runs tesseract for it. |
| `ocrmac` | docling | macOS only | Best raw accuracy and fastest of all classic engines; irrelevant for Linux deploys. |
| `rapidocr` | docling | pip-only fallback | No system packages needed, perfect on tables/CJK — but it silently shreds some long text lines into garbage at every setting tried, which is content loss for RAG ingestion. Avoid as a server default until fixed upstream. |

For `deepseek`, point the worker at an OpenAI-compatible endpoint:

```env
OCR_ENGINE=deepseek
OCR_DEEPSEEK_URL=http://localhost:11434/v1/chat/completions  # Ollama default
OCR_DEEPSEEK_MODEL=deepseek-ocr:3b
OCR_DEEPSEEK_TIMEOUT=300  # seconds per page request, both backends
```

Ollama works out of the box (`ollama pull deepseek-ocr:3b`); for real
throughput serve `deepseek-ai/DeepSeek-OCR` with vLLM on a GPU and set the
URL accordingly. The native backend sends pages one at a time, so a slow
laptop-hosted model only needs a generous `OCR_DEEPSEEK_TIMEOUT`; docling's
VLM pipeline honours the same timeout per request but keeps its own four
concurrent requests.

A selected engine that is not available degrades rather than failing parses:
under docling, a missing tesseract binary or non-macOS `ocrmac` falls back to
`auto` with a warning; under `native`, a docling-only engine becomes
tesseract, and a missing tesseract binary fails the scanned file with an
install hint (text-layer documents are unaffected).

<Callout type="warning" emoji="⚠️">
A language listed in `OCR_LANGS` whose tesseract pack is not installed fails
every scanned page loudly (tesseract exits with "Error opening data file
... chi_sim.traineddata"), on both backends. Install the pack before
listing it: `apt-get install tesseract-ocr-chi-sim` in the image, or on
macOS download `chi_sim.traineddata` into `$(brew --prefix)/share/tessdata`.
</Callout>

### DeepSeek through docling versus native

`OCR_ENGINE=deepseek` behaves differently on the two backends, and the
difference matters more than for tesseract:

- **native** sends one request per page that lacks a text layer and reads
  every other page directly. Pages go one at a time with
  `OCR_DEEPSEEK_TIMEOUT` per request, so a slow model server just takes
  longer.
- **docling** uses its VLM pipeline, which *replaces* the whole converter:
  every page of every PDF that reaches docling goes to the model, text layer
  or not (a 39-page text PDF measured 721 s against 20 s on the other paths),
  with four concurrent requests and `OCR_DEEPSEEK_TIMEOUT` per request. In testing it also
  dropped page-level elements — a title, an intro paragraph — that the same
  model kept through the native path, and it logs a burst of pydantic
  serialization warnings per conversion. Its one advantage was table
  structure: it was the only configuration that returned an invoice's
  subtotal/VAT/total rows as table rows.

Measured on the same scans (2026-09, MacBook Air, Ollama `deepseek-ocr:3b`):

| Configuration | Scanned page | Image | Accuracy | Tables | Chinese |
|---|---|---|---|---|---|
| tesseract, native | 1-2 s | 1-2 s | exact | flat lines | clean |
| deepseek, native | 15-30 s | 15-25 s | exact | Markdown table, totals as bold lines | clean |
| tesseract, docling | 3-4 s | ~8 s | exact | Markdown body, totals shredded | clean |
| deepseek, docling | ~20 s | ~20 s | drops titles/paragraphs | exact | clean |

Recommendation: `native` with `tesseract` for throughput, `native` with
`deepseek` for quality, `docling` with `tesseract` when you want its layout
model, and `docling` with `deepseek` only for table-heavy scans where the
costs above are acceptable.

## Processing Flow

### Source Docs flow (Upload and Train)

1. Files are uploaded through `/api/upload`.
2. Ingestion runs asynchronously in Celery (`ingest_worker`).
3. `SimpleDirectoryReader` parses files with `get_default_file_extractor`.
4. Documents are parsed by the `DOC_PARSER_ENGINE` engine; images (and, under `anydoc`, scanned PDFs) reach the OCR backend. OCR in this path is controlled by `OCR_ENABLED`.
5. Parsed text is chunked, embedded, and stored in the vector store.
6. Retrieval during chat uses this indexed text and returns source citations.

### Attachment flow (Chat-only file context)

1. Files are uploaded through `/api/store_attachment`.
2. Celery task `attachment_worker` parses and stores the attachment in Postgres (`attachments` table).
3. OCR in this path is controlled by `OCR_ATTACHMENTS_ENABLED`.
4. Attachments are not vectorized and are not added to the source index.
5. During answer generation, selected attachment IDs are loaded and passed directly to the LLM pipeline.

## How Docling OCR Works

With `OCR_BACKEND=docling`, OCR behavior is different for PDFs vs images:

- PDF parser defaults to hybrid OCR:
  - text regions: extracted directly
  - bitmap/image regions: OCR only where needed
- Image parser defaults to full-page OCR (the whole image is visual content).

The engine and its languages come from `OCR_ENGINE` and `OCR_LANGS` (see the
table above). The Docker image ships only the English tesseract pack; for
other languages install their packs in the image (e.g.
`apt-get install tesseract-ocr-chi-sim`) and list them in `OCR_LANGS`
(`eng+chi_sim`).

<Callout type="warning" emoji="⚠️">
Upgrading from a RapidOCR-based deployment? RapidOCR covered English *and*
Chinese with no configuration. The tesseract default only OCRs the languages
in `OCR_LANGS` (`eng` out of the box), so CJK scans stop ingesting until
their packs are installed and listed.
</Callout>

### Model compilation

Docling runs its layout, table, and OCR models through `torch.compile` by
default. DocsGPT turns that off. On x86-64 Linux, compiling raised the first
parse of a two-page PDF from 8.4s to 58.6s while steady-state parsing stayed
at 1.5s either way, so the warmup is overhead a per-file parse never recovers.
Compiling also fails outright on Apple Silicon, on install paths containing a
space, on Windows without MSVC, and in slim images with no C compiler.

```env
DOCLING_COMPILE_TORCH_MODELS=false
```

Set it to `true` only if you are parsing large batches on a machine where the
warmup pays for itself.

## Attachment Behavior by Model Support

When attachments are used in chat, behavior depends on the selected model/provider:

- If a MIME type is supported, DocsGPT sends files/images through provider-native attachment APIs.
- If unsupported, DocsGPT falls back to the parsed text content stored for the attachment.
- For providers that support images but not native PDF attachments, PDF files are converted to images (synthetic PDF support).

This means OCR quality is especially important for text fallback paths and for models without native attachment support.

## Recommended Configuration

For most OCR-enabled use cases, enable both flags and leave the backend on
`auto`:

```env
OCR_ENABLED=true
OCR_ATTACHMENTS_ENABLED=true
```

After changing these settings, restart the API and Celery worker.

## Legacy Fallback Notes

- If Docling is unavailable, DocsGPT falls back to the native OCR parsers (OCR on) or the legacy parsers (OCR off) for the formats anydoc does not cover, and anydoc's refusals of scanned PDFs become upload errors only when no OCR is available.
- With OCR disabled, text-based PDFs can still parse, but scanned/image-heavy content may produce little text.
- For image parsing without OCR, the legacy image parser only extracts text when `PARSE_IMAGE_REMOTE=true`.