Expandable and scalable OCR pipeline
Project description
Overview
Nidaba is the central controller for the entire OGL OCR pipeline. It oversees and automates the process of converting raw images into citable collections of digitized texts.
It offers the following functionality:
Grayscale Conversion
Binarization utilizing Sauvola adaptive thresholding, Otsu, or ocropus’s nlbin algorithm
Deskewing
Dewarping
Page segmentation from the aforementioned OCR packages
Various postprocessing utilities like spell-checking, merging of multiple results, and ground truth comparison.
As it is designed to use a common storage medium on network attached storage and the celery distributed task queue it scales nicely to multi-machine clusters.
Build
To easiest way to install the latest stable(-ish) nidaba is from PyPi:
$ pip install nidaba
or run:
$ pip install .
in the git repository for the bleeding edge development version.
Some useful tasks have external dependencies. A good start is:
# apt-get install libtesseract3 tesseract-ocr-eng libleptonica-dev liblept
Tests
Per default no dictionaries and OCR models necessary to runs the tests are installed. To download the necessary files run:
$ python setup.py download
$ python setup.py nosetests
Tests for modules that call external programs, at the time only tesseract, ocropus, and kraken, will be skipped if these aren’t installed.
Running
First edit (the installed) nidaba.yaml and celery.yaml to fit your needs. Have a look at the docs if you haven’t set up a celery-based application before.
Then start up the celery daemon with something like:
$ celery -A nidaba worker
Next jobs can be added to the pipeline using the nidaba executable:
$ nidaba batch -b otsu -l tesseract -o tesseract:eng -- ./input.tiff Preparing filestore [✓] Building batch [✓] 951c57e5-f8a0-432d-8d77-8a2e27fff53c
Using the return code the current state of the job can be retrieved:
$ nidaba status 25d79a54-9d4a-4939-acb6-8e168d6dbc7c PENDING
When the job has been processed the status command will return a list of paths containing the final output:
$ nidaba status 951c57e5-f8a0-432d-8d77-8a2e27fff53c SUCCESS 14.tif → .../input_img.rgb_to_gray_binarize.otsu_ocr.tesseract_grc.tif.hocr
Documentation
Want to learn more? Read the Docs
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
File details
Details for the file nidaba-0.9.7.tar.gz.
File metadata
- Download URL: nidaba-0.9.7.tar.gz
- Upload date:
- Size: 1.3 MB
- Tags: Source
- Uploaded using Trusted Publishing? No
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | a4dee6bf45e88a5c5fda6521143d53c2844c0a2fb394ef364fa9533e6e92b69b |
|
| MD5 | 283a14e15fea566d2bd611bd2cbbf12a |
|
| BLAKE2b-256 | c9f93590dfb1c934789c71a9bc85e691b9b524feb30eadda4c453c9adb5a6e9e |
File details
Details for the file nidaba-0.9.7-py2-none-any.whl.
File metadata
- Download URL: nidaba-0.9.7-py2-none-any.whl
- Upload date:
- Size: 1.3 MB
- Tags: Python 2
- Uploaded using Trusted Publishing? No
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | b696cd4274a989790371b0d9d96905eb20ce378a2555c2754d9b0e9d6772d76c |
|
| MD5 | 9808cb67e89539264dcea1a3ad20ebb6 |
|
| BLAKE2b-256 | 325fc988b3b2e7ad950629d02c77085fac106063c308c58ad664b8352e7b2baf |
