s3-ocr 0.3

Tools for running OCR against files stored in S3

s3-ocr

Tools for running OCR against files stored in S3

Installation

Install this tool using pip:

pip install s3-ocr

Starting OCR against PDFs in a bucket

The start command takes a list of keys and submits them to Textract for OCR processing.

You need to have AWS configured using environment variables or a credentials file in your home directory.

You can start the process running like this:

s3-ocr start name-of-your-bucket my-pdf-file.pdf

The paths you specify should be paths within the bucket. If you stored your PDF files in folders inside the bucket it should look like this:

s3-ocr start name-of-your-bucket path/to/one.pdf path/to/two.pdf

OCR can take some time. The results of the OCR will be stored in textract-output in your bucket.

To process every file in the bucket with a .pdf extension use --all:

s3-ocr start name-of-bucket --all

s3-ocr start --help

Usage: s3-ocr start [OPTIONS] BUCKET [KEYS]...

  Start OCR tasks for PDF files in an S3 bucket

      s3-ocr start name-of-bucket path/to/one.pdf path/to/two.pdf

  To process every file with a .pdf extension:

      s3-ocr start name-of-bucket --all

Options:
  --all                 Process all PDF files in the bucket
  --access-key TEXT     AWS access key ID
  --secret-key TEXT     AWS secret access key
  --session-token TEXT  AWS session token
  --endpoint-url TEXT   Custom endpoint URL
  -a, --auth FILENAME   Path to JSON/INI file containing credentials
  --help                Show this message and exit.

Checking status

The s3-ocr status <bucket-name> command shows a rough indication of progress through the tasks:

% s3-ocr status sfms-history
153 complete out of 532 jobs

It compares the jobs that have been submitted, based on .s3-ocr.json files, to the jobs that have their results written to the textract-output/ folder.

s3-ocr status --help

Usage: s3-ocr status [OPTIONS] BUCKET

  Show status of OCR jobs for a bucket

Options:
  --access-key ...

Fetching the results

Once an OCR job has completed you can download the resulting JSON using the fetch command:

s3-ocr fetch name-of-bucket path/to/file.pdf

This will save files in the current directory with names like this:

• 4d9b5cf580e761fdb16fd24edce14737ebc562972526ef6617554adfa11d6038-1.json
• 4d9b5cf580e761fdb16fd24edce14737ebc562972526ef6617554adfa11d6038-2.json

The number of files will vary depending on the length of the document.

If you don't want separate files you can combine them together using the -c/--combine option:

s3-ocr fetch name-of-bucket path/to/file.pdf --combine output.json

The output.json file will then contain data that looks something like this:

{
  "Blocks": [
    {
      "BlockType": "PAGE",
      "Geometry": {...}
      "Page": 1,
      ...
    },
    {
      "BlockType": "LINE",
      "Page": 1,
      ...
      "Text": "Barry",
    },

s3-ocr fetch --help

Usage: s3-ocr fetch [OPTIONS] BUCKET KEY

  Fetch the OCR results for a specified file

      s3-ocr fetch name-of-bucket path/to/key.pdf

  This will save files in the current directory called things like

      a806e67e504fc15f...48314e-1.json     a806e67e504fc15f...48314e-2.json

  To combine these together into a single JSON file with a specified name, use:

      s3-ocr fetch name-of-bucket path/to/key.pdf --combine output.json

  Use "--output -" to print the combined JSON to standard output instead.

Options:
  -c, --combine FILENAME  Write combined JSON to file
  --access-key ...

Fetching just the text of a page

If you don't want to deal with the JSON directly, you can use the text command to retrieve just the text extracted from a PDF:

s3-ocr text name-of-bucket path/to/file.pdf

This will output plain text to standard output.

To save that to a file, use this:

s3-ocr text name-of-bucket path/to/file.pdf > text.txt

Separate pages will be separated by three newlines. To separate them using a ---- horizontal divider instead add --divider:

s3-ocr text name-of-bucket path/to/file.pdf --divider

s3-ocr text --help

Usage: s3-ocr text [OPTIONS] BUCKET KEY

  Retrieve the text from an OCRd PDF file

      s3-ocr text name-of-bucket path/to/key.pdf

Options:
  --divider             Add ---- between pages
  --access-key ...

Changes made to your bucket

To keep track of which files have been submitted for processing, s3-ocr will create a JSON file for every file that it adds to the OCR queue.

This file will be called:

path-to-file/name-of-file.pdf.s3-ocr.json

Each of these JSON files contains data that looks like this:

{
  "job_id": "a34eb4e8dc7e70aa9668f7272aa403e85997364199a654422340bc5ada43affe",
  "etag": "\"b0c77472e15500347ebf46032a454e8e\""
}

The recorded job_id can be used later to associate the file with the results of the OCR task in textract-output/.

The etag is the ETag of the S3 object at the time it was submitted. This can be used later to determine if a file has changed since it last had OCR run against it.

This design for the tool, with the .s3-ocr.json files tracking jobs that have been submitted, means that it is safe to run s3-ocr start against the same bucket multiple times without the risk of starting duplicate OCR jobs.

Creating a SQLite index of your OCR results

The s3-ocr index <bucket> <database_file> command creates a SQLite database contaning the results of the OCR, and configure SQLite full-text search for the text:

% s3-ocr index sfms-history index.db
Fetching job details  [####################################]  100%
Populating pages table  [####################----------------]   55%  00:03:18

The schema of the resulting database looks like this (excluding the FTS tables):

CREATE TABLE [pages] (
   [path] TEXT,
   [page] INTEGER,
   [folder] TEXT,
   [text] TEXT,
   PRIMARY KEY ([path], [page])
);
CREATE TABLE [ocr_jobs] (
   [key] TEXT PRIMARY KEY,
   [job_id] TEXT,
   [etag] TEXT,
   [s3_ocr_etag] TEXT
);
CREATE TABLE [fetched_jobs] (
   [job_id] TEXT PRIMARY KEY
);

The database is designed to be used with Datasette.

s3-ocr index --help

Usage: s3-ocr index [OPTIONS] BUCKET DATABASE

  Create a SQLite database with OCR results for files in a bucket

Options:
  --access-key ...

Development

To contribute to this tool, first checkout the code. Then create a new virtual environment:

cd s3-ocr
python -m venv venv
source venv/bin/activate

Now install the dependencies and test dependencies:

pip install -e '.[test]'

To run the tests:

pytest

To regenerate the README file with the latest --help:

cog -r README.md