18: Application Example OCR

[ 18: Application Example OCR ]

Previous Next Index

Problem description and pipeline

  • Case study focused around photo OCR
  • Three reasons to do this
    • 1) Look at how a complex system can be put together
    • 2) The idea of a machine learning pipeline
      • What to do next
      • How to do it
    • 3) Some more interesting ideas
      • Applying machine learning to tangible problems
      • Artificial data synthesis

What is the photo OCR problem?

  • Photo OCR = photo optical character recognition
    • With growth of digital photography, lots of digital pictures
    • One idea which has interested many people is getting computers to understand those photos
    • The photo OCR problem is getting computers to read text in an image
      • Possible applications for this would include
        • Make searching easier (e.g. searching for photos based on words in them)
        • Car navigation
  • OCR of documents is a comparatively easy problem
    • From photos it’s really hard

OCR pipeline

  • 1) Look through image and find text
  • 2) Do character segmentation
  • 3) Do character classification
  • 4) Optional some may do spell check after this too
    • We’re not focussing on such systems though
  • Pipelines are common in machine learning
    • Separate modules which may each be a machine learning component or data processing component
  • If you’re designing a machine learning system, pipeline design is one of the most important questions
    • Performance of pipeline and each module often has a big impact on the overall performance a problem
    • You would often have different engineers working on each module
      • Offers a natural way to divide up the workload

Sliding window image analysis

  • How do the individual models work?
  • Here focus on a sliding windows classifier
  • As mentioned, stage 1 is text detection
    • Unusual problem in computer vision – different rectangles (which surround text) may have different aspect ratios (aspect ratio being height : width)
      • Text may be short (few words) or long (many words)
      • Tall or short font
      • Text might be straight on
      • Slanted
    • Let’s start with a simpler example
Pedestrian detection
  • Want to take an image and find pedestrians in the image
  • This is a slightly simpler problem because the aspect ration remains pretty constant
  • Building our detection system
    • Have 82 x 36 aspect ratio
      • This is a typical aspect ratio for a standing human
    • Collect training set of positive and negative examples
    • Could have 1000 – 10 000 training examples
    • Train a neural network to take an image and classify that image as pedestrian or not
      • Gives you a way to train your system
  • Now we have a new image – how do we find pedestrians in it?
    • Start by taking a rectangular 82 x 36 patch in the image

      • Run patch through classifier – hopefully in this example it will return y = 0
    • Next slide the rectangle over to the right a little bit and re-run
      • Then slide again
      • The amount you slide each rectangle over is a parameter called the step-size or stride
        • Could use 1 pixel
          • Best, but computationally expensive
        • More commonly 5-8 pixels used
      • So, keep stepping rectangle along all the way to the right
        • Eventually get to the end
      • Then move back to the left hand side but step down a bit too
      • Repeat until you’ve covered the whole image
    • Now, we initially started with quite a small rectangle
      • So now we can take a larger image patch (of the same aspect ratio)
      • Each time we process the image patch, we’re resizing the larger patch to a smaller image, then running that smaller image through the classifier
    • Hopefully, by changing the patch size and rastering repeatedly across the image, you eventually recognize all the pedestrians in the picture

Text detection example

  • Like pedestrian detection, we generate a labeled training set with
    • Positive examples (some kind of text)
    • Negative examples (not text)
  • Having trained the classifier we apply it to an image
    • So, run a sliding window classifier at a fixed rectangle size
    • If you do that end up with something like this

    • White region show where text detection system thinks text is
      • Different shades of gray correspond to probability associated with how sure the classifier is the section contains text
        • Black – no text
        • White – text
      • For text detection, we want to draw rectangles around all the regions where there is text in the image
    • Take classifier output and apply an expansion algorithm
      • Takes each of white regions and expands it
      • How do we implement this
        • Say, for every pixel, is it within some distance of a white pixel?
        • If yes then colour it white
    • Look at connected white regions in the image above
      • Draw rectangles around those which make sense as text (i.e. tall thin boxes don’t make sense)
    • This example misses a piece of text on the door because the aspect ratio is wrong
      • Very hard to read

Stage two is character segmentation

  • Use supervised learning algorithm
  • Look in a defined image patch and decide, is there a split between two characters?
    • So, for example, our first training data item below looks like there is such a split
    • Similarly, the negative examples are either empty or hold a full characters
  • We train a classifier to try and classify between positive and negative examples
    • Run that classifier on the regions detected as containing text in the previous section
  • Use a 1-dimensional sliding window to move along text regions
    • Does each window snapshot look like the split between two characters?
      • If yes insert a split
      • If not move on
    • So we have something that looks like this

Character classification

  • Standard OCR, where you apply standard supervised learning which takes an input and identify which character we decide it is
    • Multi-class characterization problem
Getting lots of data: Artificial data synthesis
  • We’ve seen over and over that one of the most reliable ways to get a high performance machine learning system is to take a low bias algorithm and train on a massive data set
    • Where do we get so much data from
    • In ML artifice data synthesis
      • Doesn’t apply to every problem
      • If it applies to your problem can be a great way to generate loads of data
  • Two main principles
    • 1) Creating data from scratch
    • 2) If we already have a small labeled training set can we amplify it into a larger training set
Character recognition as an example of data synthesis
  • If we go and collect a large labeled data set will look like this

    • Goal is to take an image patch and have the system recognize the character
    • Treat the images as gray-scale (makes it a bit easer)
  • How can we amplify this
    • Modern computers often have a big font library
    • If you go to websites, huge free font libraries
    • For more training data, take characters from different fonts, paste these characters again random backgrounds
  • After some work, can build a synthetic training set

    • Random background
    • Maybe some blurring/distortion filters
    • Takes thought and work to make it look realistic
      • If you do a sloppy job this won’t help!
      • So unlimited supply of training examples
    • This is an example of creating new data from scratch
  • Other way is to introduce distortion into existing data
    • e.g. take a character and warp it

      • 16 new examples
      • Allows you amplify existing training set
    • This, again, takes though and insight in terms of deciding how to amplify

Another example: speech recognition

  • Learn from audio clip – what were the words
    • Have a labeled training example
    • Introduce audio distortions into the examples
  • So only took one example
    • Created lots of new ones!
  • When introducing distortion, they should be reasonable relative to the issues your classifier may encounter
Getting more data
  • Before creating new data, make sure you have a low bias classifier
    • Plot learning curve
  • If not a low bias classifier increase number of features
    • Then create large artificial training set
  • Very important question: How much work would it be to get 10x data as we currently have?
    • Often the answer is, “Not that hard”
    • This is often a huge way to improve an algorithm
    • Good question to ask yourself or ask the team
  • How many minutes/hours does it take to get a certain number of examples
    • Say we have 1000 examples
    • 10 seconds to label an example
    • So we need another 9000 – 90000 seconds
    • Comes to a few days (25 hours!)
  • Crowd sourcing is also a good way to get data
    • Risk or reliability issues
    • Cost
    • Example
      • E.g. Amazon mechanical turks
Ceiling analysis: What part of the pipeline to work on next
  • Through the course repeatedly said one of the most valuable resources is developer time
    • Pick the right thing for you and your team to work on
    • Avoid spending a lot of time to realize the work was pointless in terms of enhancing performance
Photo OCR pipeline
  • Three modules
    • Each one could have a small team on it
    • Where should you allocate resources?
  • Good to have a single real number as an evaluation metric
    • So, character accuracy for this example
    • Find that our test set has 72% accuracy

Ceiling analysis on our pipeline

  • We go to the first module
    • Mess around with the test set – manually tell the algorithm where the text is
    • Simulate if your text detection system was 100% accurate
      • So we’re feeding the character segmentation module with 100% accurate data now
    • How does this change the accuracy of the overall system
    • Accuracy goes up to 89%
  • Next do the same for the character segmentation
    • Accuracy goes up to 90% now
  • Finally doe the same for character recognition
    • Goes up to 100%
  • Having done this we can qualitatively show what the upside to improving each module would be
    • Perfect text detection improves accuracy by 17%!
      • Would bring the biggest gain if we could improve
    • Perfect character segmentation would improve it by 1%
      • Not worth working on
    • Perfect character recognition would improve it by 10%
      • Might be worth working on, depends if it looks easy or not
  • The “ceiling” is that each module has a ceiling by which making it perfect would improve the system overall
Other example – face recognition
  • NB this is not how it’s done in practice

    • Probably more complicated than is used in practice
  • How would you do ceiling analysis for this
    • Overall system is 85%
    • Perfect background -> 85.1%
      • Not a crucial step
    • + Perfect face detection -> 91%
      • Most important module to focus on
    • + Perfect eyes ->95%
    • + Perfect Nose -> 96%
    • + Perfect Mouth -> 97%
    • + Perfect logistic regression -> 100%
  • Cautionary tale
    • Two engineers spent 18 months improving background pre-processing
      • Turns out had no impact on overall performance
      • Could have saved three years of man power if they’d done ceiling analysis

Author: iotmaker

I am interested in IoT, robot, figures & leadership. Also, I have spent almost every day of the past 15 years making robots or electronic inventions or computer programs.

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out /  Change )

Google photo

You are commenting using your Google account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s