/xberg-extracting-tables

description: Use when extracting tabular data from PDFs, spreadsheets, or images. Covers layout-aware table detection, table model selection, output formats (markdown / JSON cells), and known limits.
name: xberg-extracting-tables

<!-- AI-RULEZ :: GENERATED FILE — DO NOT EDIT Content-Hash: blake3:80d26904ff70da39df4ee2b35bcfc06a186b30c0d2566cf4b60832cd42e475ef Source-Hash: blake3:662aa704cf97ad20f02258c9dcd1c73822213a426e27ec7ebc292d3f599fb7c2 Schema-Version: v1 -->

Extracting tables

Use this when the user wants structured tabular data — financial statements, scientific tables, invoices, spreadsheet-style PDFs. Xberg detects tables via a layout model (RT-DETR v2) and reconstructs cell structure with a configurable table model.

Basic usage

# Markdown tables embedded in the content stream
xberg extract report.pdf --layout --content-format markdown

# Structured JSON output, tables appear under result.tables
xberg extract report.pdf --layout --format json

`--layout` turns on layout-aware extraction; without it, tables fall back to plain text reflow and you lose cell boundaries.

Output shapes

Two surfaces, picked via `--format` (CLI shape) and `--content-format` (content rendering):

• **Markdown tables in `content`** — `--content-format markdown`. Tables

appear inline as `| col | col |` blocks. Good for LLM ingestion.

• **Structured `tables` array** — `--format json`. Each entry has

`cells[][]` (rows × cols), `markdown` (pre-rendered), `page_number`, `bounding_box`. Use this when downstream code needs exact cell access. (`bounding_box` is omitted when no position data is available.)

Both are populated at once when `--layout` is on. The `tables` array is always structured; the `content` stream switches representation.

xberg extract financials.pdf --layout --format json \
  | jq '.result.tables[] | {page: .page_number, rows: (.cells | length)}'

Table models

`--layout-table-model` picks the reconstruction backend:

| Model | Best for | Notes | | ------------------ | ----------------------------------------------------- | ------------------------------------------- | | `tatr` | dense complex tables (academic, financial) | **Default.** Heaviest, highest accuracy. | | `slanet_auto` | dispatches per-table to wired/wireless | Good when table styles are mixed. | | `slanet_wired` | tables with visible borders | Faster than tatr. | | `slanet_wireless` | tables without borders (whitespace-separated) | For invoices, simple grids. | | `slanet_plus` | hybrid wired / wireless | Lighter than `slanet_auto`. | | `disabled` | layout detection only, no table structure | Use to skip table model cost. |

xberg extract bank-statement.pdf \
  --layout --layout-table-model tatr --content-format markdown

Drop `--layout-confidence` when the layout model misses tables (default threshold ~0.5):

xberg extract noisy-scan.pdf --layout --layout-confidence 0.3

Spreadsheets

`.xlsx`, `.ods`, `.csv`, `.tsv` are extracted by dedicated parsers — no layout model needed. Each sheet becomes a markdown table (or structured table) automatically:

xberg extract workbook.xlsx --content-format markdown
xberg extract data.csv --format json

Pass `--no-cache=true` only when iterating on the same file with different configs.

Config file alternative

# `output_format` in config files equals `--content-format` on the CLI.
output_format = "markdown"

[layout]
confidence_threshold = 0.5
table_model = "tatr"

Then:

xberg extract report.pdf --format json

Programmatic access

From Python, structured tables live on the document in the result envelope (`result.results[0].tables`):

from xberg import ExtractInput, extract, ExtractionConfig, LayoutDetectionConfig

config = ExtractionConfig(
    layout=LayoutDetectionConfig(table_model="tatr"),
    output_format="markdown",
)
result = await extract(ExtractInput(uri="report.pdf"), config)
for table in result.results[0].tables:
    print(table.markdown)        # rendered markdown
    print(table.cells[0][0])     # cell access

Node.js mirrors this (`extract`, `output.results[0].tables`, camelCase fields). See `references/python-api.md` and `references/nodejs-api.md` for full type signatures.

Known limitations

• **Merged cells** — reconstructed as repeated values across the spanned

region; the merge is not preserved as metadata.

• **Rotated tables** — enable `--ocr-auto-rotate true` for image-based

PDFs before extraction.

• **Nested tables** — flattened. Detection succeeds; structural nesting is

lost.

• **Multi-page tables** — each page yields a separate `tables[]` entry.

Stitch by matching column headers if needed.

• **ONNX Runtime required** — layout and table models are unavailable in

WASM builds and on the Android x86_64 emulator; native targets ship full support.

Common failure modes

• **Empty `tables` with `--layout` on** — confidence threshold too high or

table model mismatched. Drop `--layout-confidence` to 0.3, try `--layout-table-model tatr`.

• **Markdown tables look ragged** — switch `--layout-table-model` to

`slanet_wired` for bordered grids or `slanet_wireless` for invoices.

• **Slow extraction** — `tatr` is heavy. Use `slanet_auto` or

`slanet_plus` as a default; reach for `tatr` only when accuracy matters.

See `references/cli-reference.md` for the full layout flag set and `references/advanced-features.md` for the layout pipeline internals.