pypi_packages: s3-ocr
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| s3-ocr | Tools for running OCR against files stored in S3 | [] | # s3-ocr [](https://pypi.org/project/s3-ocr/) [](https://github.com/simonw/s3-ocr/releases) [](https://github.com/simonw/s3-ocr/actions?query=workflow%3ATest) [](https://github.com/simonw/s3-ocr/blob/master/LICENSE) Tools for running OCR against files stored in S3 Background on this project: [s3-ocr: Extract text from PDF files stored in an S3 bucket](https://simonwillison.net/2022/Jun/30/s3-ocr/) ## Installation Install this tool using `pip`: pip install s3-ocr ## Demo You can see the results of running this tool against three PDFs from the Internet Archive ([one](https://archive.org/details/unmaskingrobert00houdgoog), [two](https://archive.org/details/practicalmagicia00harr), [three](https://archive.org/details/latestmagicbeing00hoff)) in [this example table](https://s3-ocr-demo.datasette.io/pages/pages?_facet=path#facet-path) hosted using [Datasette](https://datasette.io/). ## Starting OCR against PDFs in a bucket The `start` command takes a list of keys and submits them to [Textract](https://aws.amazon.com/textract/) for OCR processing. You need to have AWS configured using environment variables, credentials file in your home directory or a JSON or INI file generated using [s3-credentials](https://datasette.io/tools/s3-credentials). 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 To process every file with a `.pdf` extension within a specific folder, use `--prefix`: s3-ocr start name-of-bucket --prefix path/to/folder ### s3-ocr start --help <!-- [[[cog import cog from s3_ocr import cli from click.testing import CliRunner runner = CliRunner() result = runner.invoke(cli.cli, ["start", "--help"]) help = result.output.replace("Usage: cli", "Usage: s3-ocr") cog.out( "```\n{}\n```".format(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 To process every .pdf in the PUBLIC/ folder: s3-ocr start name-of-bucket --prefix PUBLIC/ Options: --all Process all PDF files in the bucket --prefix TEXT Process all PDF files within this prefix --dry-run Show what this would do, but don't actually do it --no-retry Don't retry failed requests --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. ``` <!-- [[[end]]] --> ## 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 <!-- [[[cog result = runner.invoke(cli.cli, ["status", "--help"]) help = result.output.replace("Usage: cli", "Usage: s3-ocr") cog.out( "```\n{}\n```".format(help.split("--access-key")[0] + "--access-key ...") ) ]]] --> ``` Usage: s3-ocr status [OPTIONS] BUCKET Show status of OCR jobs for a bucket Options: --access-key ... ``` <!-- [[[end]]] --> ## Inspecting a job The `s3-ocr inspect-job <job_id>` command can be used to check the status of a specific job ID: ``` % s3-ocr inspect-job b267282745685226339b7e0d4366c4ff6887b7e293ed4b304dc8bb8b991c7864 { "DocumentMetadata": { "Pages": 583 }, "JobStatus": "SUCCEEDED", "DetectDocumentTextModelVersion": "1.0" } ``` ### s3-ocr inspect-job --help <!-- [[[cog result = runner.invoke(cli.cli, ["inspect-job", "--help"]) help = result.output.replace("Usage: cli", "Usage: s3-ocr") cog.out( "```\n{}\n```".format(help.split("--access-key")[0] + "--access-key ...") ) ]]] --> ``` Usage: s3-ocr inspect-job [OPTIONS] JOB_ID Show the current status of an OCR job s3-ocr inspect-job <job_id> Options: --access-key ... ``` <!-- [[[end]]] --> ## 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 <!-- [[[cog result = runner.invoke(cli.cli, ["fetch", "--help"]) help = result.output.replace("Usage: cli", "Usage: s3-ocr") cog.out( "```\n{}\n```".format(help.split("--access-key")[0] + "--access-key ...") ) ]]] --> ``` 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 ... ``` <!-- [[[end]]] --> ## 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 <!-- [[[cog result = runner.invoke(cli.cli, ["text", "--help"]) help = result.output.replace("Usage: cli", "Usage: s3-ocr") cog.out( "```\n{}\n```".format(help.split("--access-key")[0] + "--access-key ...") ) ]]] --> ``` 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 ... ``` <!-- [[[end]]] --> ## Avoiding processing duplicates If you move files around within your S3 bucket `s3-ocr` can lose track of which files have already been processed. This can lead to additional Textract charges for processing should you run `s3-ocr start` against those new files. The `s3-ocr dedupe` command addresses this by scanning your bucket for files that have a new name but have previously been processed. It does this by looking at the `ETag` for each file, which represents the MD5 hash of the file contents. The command will write out new `.s3ocr.json` files for each detected duplicate. This will avoid those duplicates being run those duplicates through OCR a second time should yo run `s3-ocr start`. s3-ocr dedupe name-of-bucket Add `--dry-run` for a preview of the changes that will be made to your bucket. ### s3-ocr dedupe --help <!-- [[[cog result = runner.invoke(cli.cli, ["dedupe", "--help"]) help = result.output.replace("Usage: cli", "Usage: s3-ocr") cog.out( "```\n{}\n```".format(help.split("--access-key")[0] + "--access-key ...") ) ]]] --> ``` Usage: s3-ocr dedupe [OPTIONS] BUCKET Scan every file in the bucket checking for duplicates - files that have not yet been OCRd but that have the same contents (based on ETag) as a file that HAS been OCRd. s3-ocr dedupe name-of-bucket Options: --dry-run Show output without writing anything to S3 --access-key ... ``` <!-- [[[end]]] --> ## 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: ```json { "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 containing the results of the OCR, and configures SQLite full-text search against 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): ```sql 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](https://datasette.io). ### s3-ocr index --help <!-- [[[cog result = runner.invoke(cli.cli, ["index", "--help"]) help = result.output.replace("Usage: cli", "Usage: s3-ocr") cog.out( "```\n{}\n```".format(help.split("--access-key")[0] + "--access-key ...") ) ]]] --> ``` Usage: s3-ocr index [OPTIONS] BUCKET DATABASE Create a SQLite database with OCR results for files in a bucket Options: --access-key ... ``` <!-- [[[end]]] --> ## 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 | Simon Willison | text/markdown | https://github.com/simonw/s3-ocr | Apache License, Version 2.0 | https://pypi.org/project/s3-ocr/ | https://pypi.org/project/s3-ocr/ | {"CI": "https://github.com/simonw/s3-ocr/actions", "Changelog": "https://github.com/simonw/s3-ocr/releases", "Homepage": "https://github.com/simonw/s3-ocr", "Issues": "https://github.com/simonw/s3-ocr/issues"} | https://pypi.org/project/s3-ocr/0.6.3/ | ["click (>=8.0)", "boto3", "sqlite-utils", "pytest ; extra == 'test'", "moto[s3,textract] ; extra == 'test'", "cogapp ; extra == 'test'", "pytest-mock ; extra == 'test'"] | >=3.7 | 0.6.3 | 0 |