Tesseract is an open source OCR engine originally developed between 1985-1994 and now sponsored by Google. It uses techniques like adaptive thresholding and character segmentation graphs to recognize text despite challenges like non-straight baselines and varying word spacing. Tesseract represents characters as histograms of gradients to classify them using a trained classifier. Implementing real-time OCR with Tesseract on Android requires overcoming limitations of mobile devices through techniques like multi-threading for camera and processing.

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