This presentation explains the working of OCR engine for character recognition. It demonstrates the working of the Tesseract by Google.

1. Real time OCR
using Tesseract
12BCE094
SHOBHIT CHITTORA

2. Brief History Of Tesseract
 Open Source OCR engine sponsored by Google since 2006.
 One of the most accurate open source OCR engines currently
available.
 Originally developed by HP between 1985-1994.
 Lot of it is written in C and C++.

3. TessOCR Architecture

4. Adaptive Thresholding is Essential

5. Baselines are rarely perfectly
straight

6. Spaces between words are tricky
too
 Italics, digits, punctuation all create special-case font-dependent
spacing.
 Fully justified text in narrow columns can have vastly varying spacing
on different lines.

7. Tesseract Word Recognizer

8. Outline Approximation
 Polygonal approximation is a double-edged sword.
 Noise and some pertinent information are both lost.

9. Why it’s called Tesseract?
 Elements of the polygonal approximation, clustered within a
character/font combination.
 x, y position, direction, and length (as a multiple of feature length)

10. Character Classifier (Features and
Matching)
 Static classifier uses outline fragments as features. Broken characters are
easily recognizable by a small->large matching process in classifier. (This is
slow.)
 Adaptive classifier uses the same technique!

11. Classifier as Histogram of Gradients
 Quantize character area.
 Compute gradients within.
 Histograms of gradients map to fixed dimension feature vector.

12. Character Segmentation
 Segmentation Graphs

14. Rating and Certainty
 Rating = Distance * Outline length
○ Total rating over a word (or line if you prefer) is normalized
○ Different length transcriptions are fairly comparable
 Certainty = -20 * Distance
○ Measures the absolute classification confidence
○ Surrogate for log probability and is used to decide what needs
more work.

15. Tesseract Training

16. Implementation using Tess-two( Tess
port for Android)
 The Tess-two library is an open source port of Tesseract engine for
Android.
 Only the most basic and popular functionalities are ported.
 Things such as deep neutral nets are not ported.
 A lot of tweaking is required to produce desired results.

17. DEMO

18. Implementing Real Time OCR and
challenges
 Image processing on memory limited devices is difficult.
 Limited clock speeds to process huge matrices.
 Running the Camera Surface Holder in MainUI and preprocessing
and OCR on user threads.
 Maintaining huge Bitmaps for preprocessing and sending to multiple
threads.
 Avoiding Garbage Collection of important preprocessed data.

19. Thank You