18 items tagged “ocr”
2024
Docling. MIT licensed document extraction Python library from the Deep Search team at IBM, who released Docling v2 on October 16th.
Here's the Docling Technical Report paper from August, which provides details of two custom models: a layout analysis model for figuring out the structure of the document (sections, figures, text, tables etc) and a TableFormer model specifically for extracting structured data from tables.
Those models are available on Hugging Face.
Here's how to try out the Docling CLI interface using uvx (avoiding the need to install it first - though since it downloads models it will take a while to run the first time):
uvx docling mydoc.pdf --to json --to md
This will output a mydoc.json file with complex layout information and a mydoc.md Markdown file which includes Markdown tables where appropriate.
The Python API is a lot more comprehensive. It can even extract tables as Pandas DataFrames:
from docling.document_converter import DocumentConverter converter = DocumentConverter() result = converter.convert("document.pdf") for table in result.document.tables: df = table.export_to_dataframe() print(df)
I ran that inside uv run --with docling python. It took a little while to run, but it demonstrated that the library works.
Running prompts against images and PDFs with Google Gemini.
New TIL. I've been experimenting with the Google Gemini APIs for running prompts against images and PDFs (in preparation for finally adding multi-modal support to LLM) - here are my notes on how to send images or PDF files to their API using curl and the base64 -i macOS command.
I figured out the curl incantation first and then got Claude to build me a Bash script that I can execute like this:
prompt-gemini 'extract text' example-handwriting.jpg
Playing with this is really fun. The Gemini models charge less than 1/10th of a cent per image, so it's really inexpensive to try them out.
State-of-the-art music scanning by Soundslice. It's been a while since I checked in on Soundslice, Adrian Holovaty's beautiful web application focused on music education.
The latest feature is spectacular. The Soundslice music editor - already one of the most impressive web applications I've ever experienced - can now import notation directly from scans or photos of sheet music.
The attention to detail is immaculate. The custom machine learning model can handle a wide variety of notation details, and the system asks the user to verify or correct details that it couldn't perfectly determine using a neatly designed flow.
Free accounts can scan two single page documents a month, and paid plans get a much higher allowance. I tried it out just now on a low resolution image I found on Wikipedia and it did a fantastic job, even allowing me to listen to a simulated piano rendition of the music once it had finished processing.
It's worth spending some time with the release notes for the feature to appreciate how much work they've out into improving it since the initial release.
If you're new to Soundslice, here's an example of their core player interface which syncs the display of music notation to an accompanying video.
Adrian wrote up some detailed notes on the machine learning behind the feature when they first launched it in beta back in November 2022.
OMR [Optical Music Recognition] is an inherently hard problem, significantly more difficult than text OCR. For one, music symbols have complex spatial relationships, and mistakes have a tendency to cascade. A single misdetected key signature might result in multiple incorrect note pitches. And there’s a wide diversity of symbols, each with its own behavior and semantics — meaning the problems and subproblems aren’t just hard, there are many of them.
Civic Band. Exciting new civic tech project from Philip James: 30 (and counting) Datasette instances serving full-text search enabled collections of OCRd meeting minutes for different civic governments. Includes 20,000 pages for Alameda, 17,000 for Pittsburgh, 3,567 for Baltimore and an enormous 117,000 for Maui County.
Philip includes some notes on how they're doing it. They gather PDF minute notes from anywhere that provides API access to them, then run local Tesseract for OCR (the cost of cloud-based OCR proving prohibitive given the volume of data). The collection is then deployed to a single VPS running multiple instances of Datasette via Caddy, one instance for each of the covered regions.
textract-cli. This is my other OCR project from yesterday: I built the thinnest possible CLI wrapper around Amazon Textract, out of frustration at how hard that tool is to use on an ad-hoc basis.
It only works with JPEGs and PNGs (not PDFs) up to 5MB in size, reflecting limitations in Textract’s synchronous API: it can handle PDFs amazingly well but you have to upload them to an S3 bucket yet and I decided to keep the scope tight for the first version of this tool.
Assuming you’ve configured AWS credentials already, this is all you need to know:
pipx install textract-cli
textract-cli image.jpeg > output.txt
Running OCR against PDFs and images directly in your browser
I attended the Story Discovery At Scale data journalism conference at Stanford this week. One of the perennial hot topics at any journalism conference concerns data extraction: how can we best get data out of PDFs and images?
[... 2,263 words]unstructured. Relatively new but impressively capable Python library (Apache 2 licensed) for extracting information from unstructured documents, such as PDFs, images, Word documents and many other formats.
I got some good initial results against a PDF by running “pip install ’unstructured[pdf]’” and then using the “unstructured.partition.pdf.partition_pdf(filename)” function.
There are a lot of moving parts under the hood: pytesseract, OpenCV, various PDF libraries, even an ONNX model—but it installed cleanly for me on macOS and worked out of the box.
2023
Our search for the best OCR tool in 2023, and what we found. DocumentCloud’s Sanjin Ibrahimovic reviews the best options for OCR. Tesseract scores highly for easily machine readable text, newcomer docTR is great for ease of use but still not great at handwriting. Amazon Textract is great for everything except non-Latin languages, Google Cloud Vision is great at pretty much everything except for ease-of-use. Azure AI Document Intelligence sounds worth considering as well.
How I make annotated presentations
Giving a talk is a lot of work. I go by a rule of thumb I learned from Damian Conway: a minimum of ten hours of preparation for every one hour spent on stage.
[... 2,128 words]textra (via) Tiny (432KB) macOS binary CLI tool by Dylan Freedman which produces high quality text extraction from PDFs, images and even audio files using the VisionKit APIs in macOS 13 and higher. It handles handwriting too!
2022
Building a searchable archive for the San Francisco Microscopical Society
The San Francisco Microscopical Society was founded in 1870 by a group of scientists dedicated to advancing the field of microscopy.
[... 1,845 words]Digitizing 55,000 pages of civic meetings (via) Philip James has been building public, searchable archives of city council meetings for various cities—Oakland and Alamedia so far—using my s3-ocr script to run Textract OCR against the PDFs of the minutes, and deploying them to Fly using Datasette. This is a really cool project, and very much the kind of thing I’ve been hoping to support with the tools I’ve been building.
Litestream backups for Datasette Cloud (and weeknotes)
My main focus this week has been adding robust backups to the forthcoming Datasette Cloud.
[... 1,604 words]s3-ocr: Extract text from PDF files stored in an S3 bucket
I’ve released s3-ocr, a new tool that runs Amazon’s Textract OCR text extraction against PDF files in an S3 bucket, then writes the resulting text out to a SQLite database with full-text search configured so you can run searches against the extracted data.
[... 1,493 words]2021
Organize and Index Your Screenshots (OCR) on macOS (via) Alexandru Nedelcu has a very neat recipe for creating an archive of searchable screenshots on macOS: set the default save location for screenshots to a Dropbox folder, then create a launch agent that runs a script against new files in that folder to run tesseract OCR to convert them into a searchable PDF.
2009
Google Docs OCR. Whoa, the Google Docs API just got really interesting—you can upload an image to it (POST /feeds/default/private/full?ocr=true) and it will OCR the text and turn it in to a document. Since this is Google, I imagine they’ll also be using the processed documents to further improve their OCR technology.
OCR and Neural Nets in JavaScript. John dissects the brilliant Greasemonkey script that solves simple captchas using the canvas element and HTML5’s getImageData API.
2007
tesseract-ocr. Open source OCR, sponsored by Google. I just sat in on a talk on this at OSCON and the complexity of the problem is pretty incredible.