pdfexorcist

pdfexorcist extracts tables from PDFs, scans and photos. It runs several engines to read a cell and keeps the value that a majority of them read.

uvx pdfexorcist extract report.pdf      # writes report.csv

Try it in your browser: https://pdfexorcist.in/

Docs: https://pdfexorcist.in/cli/

Install with uv

With uv (pip install works the same):

curl -LsSf https://astral.sh/uv/install.sh | sh      # macOS/Linux; Windows: winget install astral-sh.uv
uv tool install pdfexorcist                          # puts the pdfexorcist command on your PATH
uv tool install "pdfexorcist[all]"                   # with every engine (or pick: [paddleocr,ocrmac])
uvx pdfexorcist report.pdf                           # or run once without installing
uv add pdfexorcist                                   # as a library in a uv project

pdftotext requires Poppler (brew install poppler, apt install poppler-utils).

Examples

NFHS-6 fact sheet

URL='https://www.nfhsiips.in/downloadFile.php?link=NFHS-6_StateFact_Chandigarh+%28UT%29__Chandigarh+Compendium.pdf&path=assets%2Fpublication%2FNFHS-6%2FStateFact%2FChandigarh+%28UT%29%2F'

pdfexorcist inspect "$URL"                    # which pages are text, and what to run
pdfexorcist extract "$URL" --pages 9-12       # generic parser
pdfexorcist extract "$URL" --recipe examples/nfhs6/district.toml   # from a checkout

The recipe keys rows by printed indicator number and names the columns.

  pdftotext   404 readings  0.0s
  pdfplumber  404 readings  0.4s
  pymupdf     404 readings  0.0s
  camelot     384 readings  1.1s
  pdfium      384 readings  0.1s

Agreed          404  100.0% of cells
Unresolved        0
Checks      2 rules  all passed

Wrote NFHS-6_StateFact_Chandigarh (UT)__Chandigarh Compendium.csv (table layout, 101 rows).

geo,indicator_no,level,nfhs6_urban,nfhs6_rural,nfhs6_total,nfhs5_total
Chandigarh,1,state,6.1,6.1,6.1,5.9
Chandigarh,2,state,20.6,21.2,20.7,23.3
Chandigarh,3,state,11.0,8.6,10.9,10.9

examples/nfhs5/state.toml reads NFHS-5 fact sheets.

A tweeted photo

BMC posts Mumbai lake levels as a photo:

pdfexorcist extract https://x.com/mybmc/status/2106224821864698086 \
    --recipe examples/lake_photo/recipe.toml          # needs OCR engines: [all]

Reading tweet_2106224821864698086_1.jpg with 4 engines (3 must agree)
Agreed      125  100.0% of cells
Unresolved    0

lake,year,level,rise_fall_24h,useful_content_ml,pct_useful_content,today_rain_mm,total_rain_mm
Upper Vaitarna,2026,603.27,0.0,219332.0,96.6,0.0,3896.0
Modak Sagar,2026,159.48,-0.25,99153.0,76.91,0.0,3260.0
Tansa,2026,128.2,-0.03,136822.0,94.31,0.0,2970.0

The recipe uses min_unopposed = 2 for values two engines agree on when no engine disagrees.

Daily government report

Maharashtra's Water Resources Department publishes dam storage at one link:

pdfexorcist extract https://mwrdpravah.in/damsafety/control/pdfLatestReportEng \
    --recipe examples/pravah_dams/recipe.toml -o pravah.csv

Agreed        1,380  100.0% of cells
Unresolved        0
Checks      2 rules  all passed

dam,region,district,sr,date,time,dead,live_cap,gross_cap,live,gross,pct_live,pct_live_last_year
Bhatsa,Kokan,Thane,2,02/10/2026,07:26 AM,34.00,942.10,976.10,929.20,963.20,98.63,98.63
Modaksagar,Kokan,Thane,4,02/10/2026,07:33 AM,76.06,128.92,204.98,101.17,177.23,78.48,99.97
Tansa,Kokan,Thane,5,02/10/2026,07:35 AM,12.08,172.52,184.60,164.83,176.91,95.54,99.55

Details: Pravah example.

Several tables in one PDF

One recipe reads the NEET (UG) 2024 result press release tables.

pdfexorcist extract https://www.nta.ac.in/Download/Notice/Notice_20240604195244.pdf \
    --recipe examples/nta_notice/recipe.toml -o neet2024.csv

Agreed         615  100.0% of cells
Unresolved       0
Failed checks    6  agreed values that break a rule
  x Table 1: registered >= categories: 6 cells

table,row,2019,2020,2021,2022,2023,2024
highlights,Number of Candidates registered,1519375,1597435,1614777,1872343,2087462,2406079
highlights,Un-Reserved,534072,475534,46-853,565964,592110,647260
language,English,1204968,1263273,1265520,1476024,1672914,1892355

table,row,2023_registered,2023_appeared,2023_qualified,2024_registered,2024_appeared,2024_qualified
gender,Female,1184513,1156618,655599,1376863,1334982,769222
state,Total,2087462,2038596,1145976,2406079,2333297,1316268

The recipe reports the printed 46-853 value as a failed check. Details: NEET 2024 example.

A scanned list

OCR reads the NTA NEET (UG) 2026 toppers scan.

pdfexorcist extract https://cdnbbsr.s3waas.gov.in/s37bc1ec1d9c3426357e69acd5bf320061/uploads/2026/07/20260716180970800.pdf \
    --recipe examples/nta_toppers/recipe.toml --pages 1-6 -o neet_top138.csv

  paddleocr  966 readings  253.2s
  ocrmac     928 readings  7.5s
  glmocr     845 readings  5.4s

Agreed          965  99.9% of cells
Unresolved        1  engines disagreed: left blank
Checks      2 rules  all passed

sr_no,rank,percentile,category,state
1,1,99.9999,General,PUNJAB
5,5,99.99965,OBC-NCL (Central List),MAHARASHTRA
44,44,99.9976999,General,CHANDIGARH (UT)

The checks require ranks to rise and percentiles to fall. Details: NEET toppers example.

pdfexorcist.extract(url) accepts file links and downloads into $PDFEXORCIST_CACHE (default ~/.cache/pdfexorcist).

Command line

Install it, then run it on a PDF:

pip install pdfexorcist     # the fifth text engine, pdftotext, also needs poppler: brew install poppler
pdfexorcist                 # a welcome screen with the common commands

Common commands:

pdfexorcist inspect report.pdf            # text or scan? and which command to run
pdfexorcist extract report.pdf            # writes report.csv next to the PDF
pdfexorcist engines                       # which engines are installed, and how to add more

extract writes an agreed-value grid and a report.review.csv file for unresolved cells. It requires --force to overwrite an output file.

python -m pdfexorcist also works. pdfexorcist report.pdf means pdfexorcist extract report.pdf.

Recipes: save the settings for a tricky table

A recipe is a TOML file with parsing and validation settings:

pdfexorcist recipe new report.pdf         # asks; or script it with --yes and options
pdfexorcist recipe show report.recipe.toml
pdfexorcist recipe check report.recipe.toml
pdfexorcist extract next_year.pdf --recipe report.recipe.toml

Settings: recipe reference.

Inspect extracted cells

pdfexorcist show report.pdf --page 3                  # writes report.p3.show.html
pdfexorcist show report.pdf -p 3 --recipe mccd.toml --open
pdfexorcist show photo.jpg --png                      # also a static annotated PNG
pdfexorcist compare report.pdf --page 3               # opens on the engines side by side

Plug-in points

pdfexorcist has 3 steps that you can replace with a function that you write. These steps are the plug-in points. Use a plug-in point only when a default step gives incorrect results for your PDF.

1. Extractor

An extractor is one engine that reads the PDF. The vote compares the values that all the engines read.

To add an extractor, do these steps:

1. Write a function that receives the path of the PDF.
2. For each page, yield the page number and a list of lines. The first page is page 1.
3. Make each line a list of (x, text) cells. x is the horizontal position of the cell.
4. Use one unit for x in one engine. You can use points or character columns.
5. Put @register("my_ocr") above the function.
6. Give the name of the engine in methods=[...], or in --engines on the command line.

@register also adds the engine to the default engines. To prevent this, write @register("my_ocr", default=False).

If you know the box of each cell, yield pdfexorcist.cells.BoxCell(x, "text", (x0, y0, x1, y1)). pdfexorcist show uses the box to mark the cell on the page.

Two engines that use the same OCR model make the same errors. If your engine uses the same model as a different engine, put the two engines in one group of families. The two engines then have one vote.

2. Parser

The parser gives a key to each value. The vote compares only values that have the same key. The default parser, parse_rows, uses the key (page, row, col). row is the row label, and col is the position of the value in the row.

To write a parser, do these steps:

1. Write a function that receives the pages from one engine. Each page is (page_no, lines).
2. For each value, yield one dict. Put the key columns and a "value" item in the dict.
3. Give the function in parse=.
4. Give the names of the key columns in key=.

A value must get the same key from all the engines.

3. Validator

A validator is a rule that the values must obey. The validators examine the values after the vote. A validator does not remove rows. It writes its name in the failed column of each row that fails.

To write a validator, do these steps:

1. Write a function that receives the result DataFrame.
2. Return a bool Series. True shows that the row fails the rule.
3. Put @check("name of the rule") above the function. The name goes into the failed column.
4. Give the function in checks=[...].

For a rule of type "total = sum of the parts", use total_check. You do not have to write a function.

Example

This example uses the 3 plug-in points together:

from pdfexorcist import extract, register, check, total_check


# 1. extractor: an OCR engine for scanned pages
@register("my_ocr")
def my_ocr(pdf):
    yield page_no, [[(x, "text"), ...], ...]  # lines of (x, text) cells


# 2. parser: one key for each value in your layout
def my_parser(pages):
    for page_no, lines in pages:
        ...
        yield {
            "page": page_no,
            "cause": "A40-A41",
            "sex": "M",
            "col": 3,
            "value": "51210",
        }


# 3. validator: a rule that the values must obey
@check("septicaemia <= its chapter")
def septic(df):
    return df.value.astype(float) > ...  # True = row fails


res = extract(
    "report.pdf",
    parse=my_parser,
    key=["page", "cause", "sex", "col"],
    checks=[septic, total_check("sex", "T", ["M", "F"], by=["cause", "col"], op=">=")],
)
res[res.failed != ""]  # values that failed a rule

Scanned PDFs

pdfexorcist also handles PDFs whose text layer comes from OCR.

extract(
    "scan.pdf",
    methods=["pdfplumber", "tesseract", "chandra"],
    parse=parse_by_columns,
    families={"tesseract": ["pdfplumber", "tesseract"]},
)

Images (photos, screenshots, tweeted scans)

extract() accepts image files with OCR engines:

extract(
    "report.jpg",
    methods=["chandra", "glmocr", "paddleocr", "ocrmac"],
    min_agree=3,
    parse=my_parser,
)

Recipe reference

A recipe is a TOML file: key = value lines under [section] headers, text in quotes, lists in brackets, # starts a comment. Every section is optional; a missing setting keeps the default, as pdfexorcist extract does without a recipe. pdfexorcist recipe check FILE names the line of any mistake. Command-line options (--pages, --engines, ...) override the recipe. Every setting is shown below; a real recipe uses a few (some exclude others, e.g. function and key belong to type = "custom").

name = "Crime in India: State/UT table"
description = "Cases by State/UT"
sample = "report.pdf"            # the file it was made with (for your reference)

[pages]                          # default: every page
select = "3-5, 9"                # page numbers; "21-" runs to the last page
start = 'TABLE\s*4\s*[-:.].*STATES'  # or: from the first page whose text matches,
stop = 'TABLE\s*5'               #   up to (not including) the next page that matches
match = 'All India'              # only pages whose text matches
fit_boxes = false                # default true: widen a page to cover text drawn outside
                                 #   it; false when that text is another page's

[engines]
use = ["pdfplumber", "pymupdf", "pdfium"]  # default: the installed default engines
ocr = false                      # true: add the installed OCR engines (when use is not given)
min_agree = 3                    # default: a strict majority, at least 3 (2 with families or only OCR engines)
min_unopposed = 2                # also accept a value this many read when none read another (off)
families = { ocr_layer = ["pdfplumber", "pymupdf"] }  # engines that are not independent vote once

[normalize]                      # rewrite every value before the vote
remove_commas = true             # "1,020" -> "1020"
remove_footnote_marks = true     # "490*" -> "490" (a lone "*" placeholder stays)
raised_dots = true               # "65·3" -> "65.3"
minus_signs = true               # "−4" -> "-4"
function = "clean.py:fix"        # your own: value -> new value, or None to drop it

[parser]
type = "rows"                    # "rows": a label, then values in reading order (default)
                                 # "columns": values placed by their position (scans, photos, blank cells)
                                 # "custom": your own function (below)
min_values = 2                   # skip lines with fewer values (titles, footnotes)
tolerance = 0.45                 # "columns" only: how far from a column a value may sit
function = "my_parser.py:parse"  # "custom": pages -> one dict per value
key = ["page", "code", "sex", "col"]  # "custom": the columns that identify a cell

[[checks]]                       # one [[checks]] block per rule; repeat it for more
column = "label"                 # the column naming the total and its parts
rule = "TOTAL ALL INDIA = TOTAL STATE(S) + TOTAL UT(S)"
by = ["page", "col"]             # checked separately within each of these
where = "col <= 2"               # optional: only the cells this pandas query keeps
tolerance = 0.01                 # optional: allowed relative gap (1%)
abs_tolerance = 0.15             # optional: allowed absolute gap (rounded numbers)
missing = "fail"                 # optional: a listed part that is missing fails (default "skip")
name = "States + UTs = India"    # optional: the name written in the failed column

[[checks]]                       # the same rule as total and parts, for a total column:
column = "col"
total = 0                        #   the total's name (text or a number)
parts = [3, 6]                   #   its parts; leave out for "every other"
op = "="                         #   =, >= or <= (default =); not used with rule
value = "value"                  #   the column holding the numbers (default "value")

[[checks]]
function = "my_checks.py:rules"  # your own check, a list of checks, or a function returning them

[postprocess]
function = "my_parser.py:name_states"  # after the vote: (cells, pdf) -> cells, e.g. add a State column

[output]
layout = "table"                 # "table": a grid of agreed values (default); "cells": one row per cell
columns = "col"                  # the column spread across the grid (default "col")
rows = ["code", "sex"]           # the columns that name a grid row (default: the rest of the key)
format = "csv"                   # csv, xlsx, parquet or json

Rules: rule is the total, then =, >= or <=, then its parts joined by +: "T = M + F", "T >= M + F" (the total may exceed its parts), or "All India = rest" for every other row of the group. Values that break a rule are kept, named in the failed column and listed for review, and the run exits with code 1. A rule whose total or parts are missing logs a warning that names them. The generic parsers name cells by page, row, col (the value's place in its line, from 0) and label (the line's text): a total row is column = "label", a total column is column = "col" with by = ["page", "row"].

Your own Python: function = "file.py:name" loads name from a .py file in the recipe's folder (or below it); that file may import its neighbours. The functions have the library's signatures: a parser takes (page_no, lines) pairs and yields dicts with the key columns and value (see examples/mccd/mccd_table4.py); a check takes the agreed cells and returns True where a row fails (see checks.py); a postprocess step takes the cells and the PDF the engines read (only the recipe's pages, numbered from

1. and returns the cells. Page numbers in the output are the original ones. A recipe that names a .py file runs that code: only use recipes you trust.

Engines

Development and releases

Use uv pip install -e . --group dev. See CHANGELOG.md.

From a checkout (local testing)

git clone https://github.com/saketkc/pdfexorcist && cd pdfexorcist
uv venv && source .venv/bin/activate     # Windows: .venv\Scripts\activate
uv pip install -e . --group dev          # editable install + pytest, ruff, mypy, parquet/xlsx writers
uv pip install -e ".[paddleocr,ocrmac]"        # optional: OCR engines to test

pdfexorcist extract tests/fixtures/srs/srs_2019-23.pdf --recipe examples/srs_life_table/recipe.toml
uvx --from . pdfexorcist --help          # or run the checkout without a venv

pytest -q tests                          # the suite; OCR tests are skipped
PDFEXORCIST_OCR_TESTS=1 pytest -q tests  # also the slow OCR tests (needs the OCR extras)
ruff check pdfexorcist tests examples .github/scripts      # lint, as CI runs it
ruff format --check pdfexorcist tests examples .github/scripts
mypy pdfexorcist

Each examples/ recipe runs on a fixture in tests/fixtures/.