pdf-parsing

name	pdf-parsing
description	Choose between docling, liteparse and pdfplumber for PDF extraction — tables, layout, fonts, screenshots, vector graphics.

PDF Parsing — docling + liteparse + pdfplumber

Three tools for PDF extraction. Each has a sweet spot — use the right one (or combine them).

When to use what

Task	Tool
Tables (especially borderless financial)	docling — AI layout model (TableFormer) handles ársreikningar with no visible borders
Structured document (headings, sections, reading order)	docling — classifies every element with semantic labels
Figure/image extraction as PIL images	docling with generate_picture_images=True
Markdown/HTML export	docling — export_to_markdown(), export_to_html()
Text with font info (name, size per fragment)	liteparse — .fontName, .fontSize on each TextItem
Page screenshots at configurable DPI	liteparse — parser.screenshot()
Quick text extraction with coordinates	liteparse — fast, simple API
Vector graphics detection (rects, curves, lines)	pdfplumber — detects infographic vs text pages
Character-level access	pdfplumber
DOCX, PPTX, XLSX, HTML	docling — same API for all formats

docling (primary tool)

IBM's AI-powered document parser. 97.9% table accuracy on benchmarks. The heavy hitter.

uv pip install docling   # v2.74.0

Basic usage

from docling.document_converter import DocumentConverter, PdfFormatOption
from docling.datamodel.pipeline_options import PdfPipelineOptions
from docling.datamodel.base_models import InputFormat

# Configure — ALWAYS disable OCR for native/digital PDFs
po = PdfPipelineOptions()
po.do_ocr = False                      # most Icelandic public PDFs are native text
po.do_table_structure = True           # TableFormer — the killer feature
po.generate_picture_images = True      # extract figures as PIL images

converter = DocumentConverter(
    format_options={InputFormat.PDF: PdfFormatOption(pipeline_options=po)}
)

result = converter.convert("report.pdf")
doc = result.document

Exports

doc.export_to_markdown()    # clean structured markdown
doc.export_to_text()        # plain text
doc.export_to_html()        # full HTML
doc.export_to_dict()        # JSON-serializable dict

# Save with images
doc.save_as_markdown("out.md", image_mode=ImageRefMode.REFERENCED)
doc.save_as_json("out.json")   # images base64-embedded by default

# Per-page markdown
doc.export_to_markdown(page_no=3)

Tables

for table in doc.tables:
    df = table.export_to_dataframe(doc)   # pandas DataFrame
    md = table.export_to_markdown(doc)    # markdown table
    html = table.export_to_html(doc)      # HTML table

    # Cell-level access
    for row in table.data.grid:
        for cell in row:
            print(cell.text, cell.column_header, cell.row_header)
            print(cell.bbox)  # BoundingBox(l, t, r, b)

docling finds tables that pdfplumber misses entirely — borderless financial tables where alignment is the only structure. Critical for Icelandic ársreikningar.

Figures

for pic in doc.pictures:
    if pic.image:
        pil_img = pic.image.pil_image   # PIL.Image (property, not method)
        pil_img.save(f"figure_{pic.self_ref}.png")
    # Position
    for prov in pic.prov:
        print(f"Page {prov.page_no}, bbox: {prov.bbox}")

Document tree & element types

from docling.datamodel.document import DocItemLabel

for item, level in doc.iterate_items():
    label = item.label  # DocItemLabel enum
    # Labels: text, section_header, title, list_item, table, picture,
    #         caption, footnote, page_header, page_footer, chart, formula, code
    if hasattr(item, 'prov') and item.prov:
        bbox = item.prov[0].bbox   # BoundingBox(l, t, r, b, coord_origin=BOTTOMLEFT)
        page = item.prov[0].page_no

# Heading levels
for item, level in doc.iterate_items():
    if isinstance(item, SectionHeaderItem):
        print(f"H{item.level}: {item.text}")

Page range + batch

# Parse only pages 1-10
result = converter.convert("big.pdf", page_range=(1, 10))

# Batch convert
for result in converter.convert_all(["a.pdf", "b.pdf", "c.pdf"]):
    print(result.document.name, result.status)

Pipeline options reference

Option	Default	Notes
do_ocr	True	Disable for native PDFs — saves ~3-5s/page
do_table_structure	True	TableFormer (ACCURATE mode). Set mode=TableFormerMode.FAST for speed
generate_picture_images	False	Extract PictureItem as PIL images
generate_page_images	False	Full-page rasters
do_picture_classification	False	AI picture type classification
do_picture_description	False	VLM captioning (SmolVLM-256M)
do_chart_extraction	False	Chart data extraction

Confidence scores

conf = result.confidence
print(conf.parse_score)    # 1.0 = native PDF parsed cleanly
print(conf.layout_score)   # 0.8–0.9 typical
print(conf.mean_grade)     # QualityGrade.GOOD / EXCELLENT

Icelandic OCR (scanned PDFs only)

from docling.datamodel.pipeline_options import OcrMacOptions  # macOS

po.do_ocr = True
po.ocr_options = OcrMacOptions(lang=["is-IS", "en-US"])  # Apple Vision
# Tesseract alternative: lang=["isl"] (needs isl.traineddata)

Most Icelandic public PDFs (skatturinn, sedlabanki, Hagstofan) are native text — do_ocr=False is correct.

Performance (M-series Mac)

Config	32 pages
No OCR, no tables	~8s
No OCR, with tables	~15-25s
First call (model loading)	+5-10s overhead

---

liteparse

LlamaIndex's fast local parser. Best for: font-level detail, page screenshots, quick text+coords.

from liteparse import LiteParse

parser = LiteParse()

# Parse (no OCR for native PDFs)
result = parser.parse("doc.pdf", ocr_enabled=False)

for page in result.pages:
    for item in page.textItems:
        # Per-fragment font info — docling doesn't have this
        print(item.text, item.x, item.y, item.fontName, item.fontSize)

CLI

lit parse doc.pdf --format json --no-ocr -o out.json
lit parse doc.pdf --target-pages "1-5" --ocr-language is
lit screenshot doc.pdf -o ./shots --dpi 200
lit batch-parse ./pdfs ./outputs --recursive

Screenshots (docling can't do this as easily)

result = parser.screenshot("doc.pdf", output_dir="./shots", dpi=200)
for s in result.screenshots:
    print(f"Page {s.page_num}: {s.image_path}")

Data model

• ParseResult.pages → list of ParsedPage
• ParsedPage: .pageNum, .width, .height, .text, .textItems, .boundingBoxes
• TextItem: .text, .x, .y, .width, .height, .fontName, .fontSize, .confidence
• BoundingBox: .x1, .y1, .x2, .y2

Coordinates: top-left origin, PDF points (1pt = 1/72 inch). A4 = 595 x 842.

---

pdfplumber

Low-level PDF access. Best for: vector graphics detection, character-level parsing, line/rect/curve geometry.

import pdfplumber

with pdfplumber.open("doc.pdf") as pdf:
    for page in pdf.pages:
        # Vector graphics — detect infographics
        images = page.images or []     # embedded raster images
        rects = page.rects or []       # rectangles
        curves = page.curves or []     # bezier curves (SVG-like vector art)
        lines = page.lines or []       # line segments

        # Page type heuristic
        text = page.extract_text() or ""
        if len(curves) > 10 and not images:
            page_type = "infographic"
        elif images and len(text) < 200:
            page_type = "photo"
        elif images:
            page_type = "mixed"
        else:
            page_type = "text"

pdfplumber's table extraction (page.extract_tables()) requires visible borders — it fails on borderless financial tables. Use docling for those.

---

Combined extraction pattern

import liteparse
import pdfplumber
from docling.document_converter import DocumentConverter, PdfFormatOption
from docling.datamodel.pipeline_options import PdfPipelineOptions
from docling.datamodel.base_models import InputFormat

def extract_pdf(path):
    """Full extraction: structure (docling) + fonts (liteparse) + graphics (pdfplumber)."""

    # 1. docling — structure, tables, figures
    po = PdfPipelineOptions(do_ocr=False, generate_picture_images=True)
    converter = DocumentConverter(
        format_options={InputFormat.PDF: PdfFormatOption(pipeline_options=po)}
    )
    doc_result = converter.convert(path)
    doc = doc_result.document

    # 2. liteparse — font info + screenshots
    lp = liteparse.LiteParse()
    lp_result = lp.parse(path, ocr_enabled=False)
    screenshots = lp.screenshot(path, output_dir="/tmp/shots", dpi=200)

    # 3. pdfplumber — vector graphics detection
    with pdfplumber.open(path) as pdf:
        for i, page in enumerate(pdf.pages):
            curves = page.curves or []
            if len(curves) > 10:
                print(f"Page {i+1}: infographic ({len(curves)} curves)")

    return {
        "markdown": doc.export_to_markdown(),
        "tables": [t.export_to_dataframe(doc) for t in doc.tables],
        "figures": [p.image.pil_image for p in doc.pictures if p.image],
        "font_map": {
            p.pageNum: [(t.text, t.fontName, t.fontSize) for t in p.textItems]
            for p in lp_result.pages
        },
        "screenshots": {s.page_num: s.image_path for s in screenshots.screenshots},
    }

Caveats

• Ligature artifacts: Some PDFs encode ff → f/f_short in font — all three tools pass this through. Not fixable without font-level glyph remapping.
• Icelandic numbers: Financial PDFs use dot-thousands (1.279.828) and parenthetical negatives (1.279.828). All tools preserve these as strings — post-process with text.replace(".", "").replace("(", "-").replace(")", "").
• Coordinate systems differ: liteparse uses top-left origin; docling BoundingBox uses bottom-left (coord_origin=BOTTOMLEFT). Convert with top_y = page_height - bbox.b.
• docling model loading: First call in a process loads TableFormer (~5-10s). Reuse the DocumentConverter instance.
• liteparse under the hood: Calls npx @llamaindex/liteparse — requires Node.js.