1. P OTENTIAL OCR S OFTWARE FOR N UTRITION F ACTS L ABELS Dennis Given

2. T HE G ENERAL O PTICAL C HARACTER R ECOGNITION C ONCEPT Input OCR buzz Output

3. P REFERENCES FOR THE OCR S Accurate Fast Written in Java This will make it easier to find someone to work on the software in the future. Open-source (free) Commercial options, although considerably faster and more accurate, are costly solutions. Editable So that if I have to, I can go into the OCR engine and edit whatever I have to. Commercial OCRs don’t always allow for this option.

4. OCR S T HAT M EET S OME OF T HE P REFERENCES Aspire OCR SDK Java OCR ABBYY FineReader Tesseract Version 3.01

5. C OMPARISONS Preferences: Accuracy? Speed? Java? Editable? Open-source? Example image to determine the best: GIF Image 1204x2004 image - this resolution is close to the resolution of the iPhone 3GS camera phone (1500x2000) and the iPhone 3G resolution (1200x1600) images.

6. E XAMPLE I MAGE 2004 pixels 1200 pixels

7. A SPIRE OCR Pros: Runs across many platforms Relatively fast Written in Java and meant to be added to Java applications Cons: Not very accurate. Must pay for the full SDK (Software Development Kit).

8. A SPIRE R ESULTS

9. J AVA OCR Pros: Written entirely in Java Full source code is given (easy to edit) Easy graphical user interface Relatively fast Cons: Instead of converting the image to text, it converts it to.png files by character Not very accurate (sometimes won’t even bother converting the image) to more than one character Even the images that were converted were not done very well…

10. J AVA OCR R ESULTS

11. ABBYY F INE R EADER Pros: Very good interface Lots of tools to edit the area being scanned The most accurate program tried Cons: Not in Java Commercial (not open-source) and VERY expensive

12. ABBYY F INE R EADER R ESULTS

13. T ESSERACT V ERSION 3.01 Developed by HP Labs Now used by Google Pros: Close in accuracy to the commercial OCRs Easy to use from the command line Lots of documentation available Cons: Must use a Java Wrapper if we want future edits to be done in Java Source code is written in C/C++ - will be difficult to edit

14. T ESSERACT R ESULTS

15. C OMMERCIAL OCR VS. T ESSERACT 100+ languages Accuracy is good Sophisticated application with complex user interface Mostly meant for Windows OS Costs $100+ to use 6+ languages Accuracy is good, but not as good as commercial OCRs No user interface Runs on Linux, Mac, Windows, and more… Open Source – Free!

16. W HERE T O G O F ROM HERE … Tesseract is our best option at this point. It is… Fast Free Outperforms the other available open-source OCR engines Plenty of documentation An Overview of the Tesseract OCR Engine by Ryan Smith Tesseract OSCON pdf http://code.google.com/p/tesseract-ocr/ Three different ways to go

17. O PTION 1: ~5 WEEKS U SE T ESSERACT E NGINE AND W RAP I T Wrapper Library A collection of subroutines or classes used to develop software. Libraries expose interfaces which clients of the library use to execute library routines. Wrapper libraries (or library wrappers) consist of a thin layer of code which translates a library's existing interface into a compatible interface. By wrapping Tesseract, it won’t matter that Tesseract’s source code is written in C++ However, this means we will still not be able to customize the Tesseract engine to do exactly what we want (specific to Nutrition labels). We can control the input and output, but the process of determining characters will remain the same.

18. O PTION 2: ~7 WEEKS B UILD AN OCR E NGINE F ROM S CRATCH Understand general concepts Can use ideas and implementations from OCRs such as Tesseract and JavaOCR. Can customize the engine to run specifically for nutrition facts labels. Would be more effective than a “general” OCR which isn’t looking for specifics. The whole thing can be written in Java (easier for future developers to work on). However: It will take more time Will probably have more bugs in it Option 3 is to take more time to determine the OCR…

19. G OALS At the end of the time frame, I plan to have: A running OCR application that will: At least be able to scan in cereal box (flat) images effectively and convert the labels to usable data. Have minimal bugs (although some will definitely exist). Have an accuracy rate of at least 95%. Begin to identify effective ways to manage images with curved (jars, bottles, etc.) and wrinkled (bags, packaging, etc.) nutrition facts labels.