Scalable OCR with NiFi and Tesseract
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This document describes a scalable optical character recognition (OCR) pipeline using Apache NiFi and Tesseract to extract text from PDF documents at large scale. The pipeline includes steps for converting PDFs to images, preprocessing the images using ImageMagick to enhance text, and performing OCR with Tesseract to extract text. NiFi is used to operationalize the pipeline and handle large-scale processing. Example use cases discussed include analyzing medical records and extracting text from large datasets to enable better journalism.
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©
Hortonworks
Inc.
2011
–
2016.
All
Rights
Reserved
OCR
At
Scale:
Use-‐cases
in
Journalism
à The
Problem
– Journalists
are
now
asked
to
analyze
large
volumes
of
data
– The
Panama
Papers
alone
were
2.6TB
of
data,
much
of
it
in
scanned
images
of
pages
– FOIA
requests
can
quickly
outstrip
the
reading
capability
of
a
single
person
or
team
à The
Value
ProposiAon
– Building
a
scalable
data
pipeline
to
extract
the
text
from
the
data
journalists
are
asked
to
mine
enables
more
advanced
analyAcs
and
be]er
reporAng.
– This
is
a
tool
to
enable
be]er
journalism](https://image.slidesharecdn.com/june29550hortonworksmiklavcicstella-160711180837/85/Scalable-OCR-with-NiFi-and-Tesseract-5-320.jpg)
![15
©
Hortonworks
Inc.
2011
–
2016.
All
Rights
Reserved
Ques>ons?
All
of
this
sorware
shown
in
this
presentaAon
is
open
source
and
located
at
h]ps://github.com/mmiklavc/scalable-‐ocr
Find
us
on
Twi]er
@casey_stella
@MikeMiklavcic](https://image.slidesharecdn.com/june29550hortonworksmiklavcicstella-160711180837/85/Scalable-OCR-with-NiFi-and-Tesseract-15-320.jpg)
Scalable OCR with NiFi and Tesseract
- 1. Scalable OCR With NiFi & Tesseract Casey Stella & Michael Miklavcic
- 2. 2 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Introduc>on à Casey Stella – Currently a data scienAst on Apache Metron – Previously Architect in Hortonworks Professional Services à Michael Miklavcic – Currently an engineer on Apache Metron – Previously Architect in Hortonworks Professional Services About the Speakers
- 3. 3 © Hortonworks Inc. 2011 – 2016. All Rights Reserved OCR At Scale: The Challenge à Unstructured data is growing aggressively à Much of this data is in the form of PDF images of text – This appears to be the case inside of organizaAons much more than on the internet à There is much we can do to extract meaning from this – NLP is one of our most mature and rich branches of machine learning – Simple textual analysis would be sufficient to have rich insights à OCR enables us to extract textual informaAon from images in an intelligent way
- 4. 4 © Hortonworks Inc. 2011 – 2016. All Rights Reserved OCR At Scale: Use-‐cases in Medicine à The Problem – Radiologists make notes about paAents – Doctors interpret these notes and make diagnoses based on the radiologist findings – SomeAmes, the radiologists find things that are serendipitous or are not definiAve. à The Value ProposiAon – Building a data pipeline at scale to analyze radiologist reports and look for indicaAons of missed diagnoses – This is correct place for advanced analyAcs: in the loop with humans
- 5. 5 © Hortonworks Inc. 2011 – 2016. All Rights Reserved OCR At Scale: Use-‐cases in Journalism à The Problem – Journalists are now asked to analyze large volumes of data – The Panama Papers alone were 2.6TB of data, much of it in scanned images of pages – FOIA requests can quickly outstrip the reading capability of a single person or team à The Value ProposiAon – Building a scalable data pipeline to extract the text from the data journalists are asked to mine enables more advanced analyAcs and be]er reporAng. – This is a tool to enable be]er journalism
- 6. 6 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Methodology : OCR à Conversion – Take PDF’s and turn them into TIFF files, page-‐wise – GhostScript via Ghost4j à Preprocessing – Prepare images by enhancing text and cleaning up arAfacts – Enable cleaner text extracAon – A preprocessing pipeline using ImageMagick under the hood à ExtracAon – OCR phase using Tesseract
- 7. 7 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Image Preprocessing à ImageMagick is a standard open source library and tool to do rich and robust image processing. à ImageMagick is great J – There is a large and mature community of users – It has been around for years and has all the primiAves that you could ask for à ImageMagick is confusing K – Image preprocessing can be a daunAng task for the user – ImageMagick can be arcane at Ames
- 8. 8 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Image Preprocessing à Community + ImageMagick = Magical – People have started making layers on top of ImageMagick to do common tasks aimed at a certain domain – Fred Weinhaus did this for text cleaning! à What we did is port this interface over to Java and expose it as a library à It currently supports – UnrotaAon (i.e. straightening images) – Greyscale – Enhance brightness – Text Smoothing – More!
- 9. 9 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Preprocessing -‐ Before and AJer -‐g -‐e stretch -‐f 25 -‐o 20 -‐t 30 -‐u -‐s 1 -‐T -‐p 20
- 10. 10 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Methodology : Scale à Apache Nifi is an easy-‐to-‐use, highly customizable data processing system firmly integrated with the Hadoop Ecosystem – Configurable prioriAzaAon, throughput/latency tradeoffs – Full data provenance across the pipeline – Easy to use interface for customizing the pipeline à Each of the phases in the pipeline becomes NIFI Processors – This allows for a highly customizable tool
- 11. 11 © Hortonworks Inc. 2011 – 2016. All Rights Reserved NiFi + Hadoop
- 12. 12 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Pipeline Architecture
- 13. 13 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Demo
- 14. 14 © Hortonworks Inc. 2011 – 2016. All Rights Reserved OCR is necessary, but not sufficient à Providing this kind of uAlity is a necessary step, but there are missing pieces à Does not handle human handwriAng as of yet – Deep learning advances are closing the gap on this à Even with very good image preprocessing, errors can creep into documents – Kerning errors : rn -‐> m – Unresolvable blemishes leading to random noise à Good error correcAon can require advanced NLP and can be domain specific – See patent #20160019430: “Targeted opAcal character recogniAon for medical terminology”
- 15. 15 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Ques>ons? All of this sorware shown in this presentaAon is open source and located at h]ps://github.com/mmiklavc/scalable-‐ocr Find us on Twi]er @casey_stella @MikeMiklavcic