Farsi Handwritten Word Recognition Using Continuous Hidden Markov Models and Structural Features

1. Farsi Handwritten Word Recognition Using Continuous Hidden Markov Models and Structural Features M. M. HajiCSE DepartmentShiraz University January 2005

2. Outline • Introduction • Preprocessing • Text Segmentation • Document Image Binarization • Skew and Slant Correction • Skeletonization • Structural Feature Extraction • Multi-CHMM Recognition • Conclusion and Discussion

3. Introduction • One of the most challenging problems in Artificial Intelligence. • Words are rather complex patterns, having much variability in handwriting style. • Performance of handwriting recognition systems is still far from human's both in terms of accuracy and speed.

4. Introduction • Previous Research: • Dehghan et al. (2001). "Handwritten Farsi (Arabic) Word Recognition: A Holistic Approach Using Discrete HMM", Pattern Recognition, vol. 34, pp. 1057-1065. • Dehghan et al. (2001). "Unconstrained Farsi Handwritten Word Recognition Using Fuzzy Vector Quantization and Hidden Markov Models", Pattern Recognition Letters, vol. 22, pp. 209-214. • A maximum recognition rate of 65% for a 198-word lexicon!

5. Methodology • Holistic Strategies • AnalyticalStrategies Implicit Segmentation Explicit Segmentation

6. Holistic Strategies • Recognition on the whole representation of a word. • No attempt to segment a word to its individual characters. • Necessary to segment the text lines into words. • Intra-word space is sometimes greater than inter-word space!

7. Holistic Strategies • Using a lexicon, a list of the allowed interpretations of the input word image. • The error rate increases with the lexicon size. • Successful for postal address recognition or bank check reading where lexicon is limited and small.

8. Analytical Strategies Explicit Segmentation: • Isolating single letters which are then separately recognized usually by neural networks . • Successful for English machine-printed text. • Arabic/Farsi texts whether machine-printed or handwritten are cursive. • Cursiveness and character overlapping are the main challenges.

9. Analytical Strategies Implicit Segmentation: • Converting the text (line or word) image into a sequence of small size units. • Recognition at this intermediate level rather than the word or character level usually by Hidden Markov Model (HMM). • Each unit may be a part of a letter, so a number of successive units can belong to a single letter.

10. Text Segmentation

11. Text Segmentation • Detecting text regions in an image (removing non-text components). • Applications in document image analysis and understanding, image compression and content-based image retrieval. • Document image binarization and skew correction algorithms usually require predominant text area to have an accurate estimate of text characteristics. • Numerous methods have been proposed (an extensive literature). • There is no general method to detect arbitrary text strings. • In the most general form, detection must be: • insensitive to noise, background model and lighting conditions and, • invariant to text language, color, size, font and orientation even in a same image!

12. Text Segmentation • We believe that a text segmentation algorithm should have adaptation and learning capability. • A learner usually needs much time and training data to achieve satisfactory results, which restricts its practicality. • A simple procedure was developed for generating training data from manually segmented images. • A Naive Bayes Classifier (NBC) was utilized, which is fast both in training and application phase. • Surprisingly excellent results were obtained by this simple classifier!

13. Text Segmentation • DCT-18 features • 10,000 training instance • Naive Bayes Classification:

14. Text Segmentation • Naive Bayes Classification: P(Text) = P(Non-text) = 0.5.

15. Binarization

16. Binarization • Converting gray-scale images into two-level images. • Many vision algorithms and operators only handle two-level images. • Applied in primary steps of a vision algorithm. • Selecting a proper threshold surface. • Challenging for images with poor contrast, strong noise and variable modalities in histograms. • Global and local (adaptive) algorithms. • General and special-purpose algorithms.

17. Binarization Four different algorithms for document image binarization were compared and contrasted: • Otsu, N. (Jan. 1979). “A Threshold Selection Method from Gray Level Histograms”, IEEE Trans. on Systems, Man and Cybernetics, vol. 9, pp. 62-66. • Niblack, W. (1989).An Introduction to Digital Image Processing, Prentice Hall, Englewood Cliffs, pp. 115-116. • Wu, V. and Manmatha, R. (Jan. 1998). "Document Image Clean-Up and Binarization", Proceedings of SPIE conference on Document Recognition. • Liu, Y. and Srihari, S. N. (May 1997). “Document Image Binarization Based on Texture Features”, IEEE Trans. on PAMI, vol. 19(5), pp. 540-544. global, general purpose local, general-purpose local, special-purpose global, special-purpose

18. Binarization Input Histogram Niblack Otsu Wu and Manmatha Liu and Srihari

19. Binarization • Quality improvement by preprocessing and postprocessing. • Preprocessing: • Taylor, M. J. and Dance, C. R. (Sep. 1998). "Enhancement of Document Images from Cameras", Proceedings of SPIE conference on Document Recognition, pp. 230-241. • Postprocessing: • Trier, D. and Taxt, T. (March 1995). "Evaluation of Binarization Methods for Document Images", IEEE Trans. on PAMI, vol. 17(3), pp. 312-315. super-resolution

20. Skew Correction

21. Skew Correction • The angle that text lines deviate from the x-axis. • Page decomposition techniques require properly aligned images as input. • 3 types: • global skew • multiple skew • non-uniform skew • “Skew correction" is applied by a rotation after "skew detection“.

22. Skew Correction • Categories based on the underlying techniques: • Projection Profile • Correlation • Hough Transform • Mathematical Morphology • Fourier Transform • Artificial Neural Networks • Nearest-Neighbor Clustering

23. Skew Correction • The projection profile at the global skew angle of the document has narrow peaks and deep valleys.

24. Skew Correction • Projection profile technique: goodness measure

25. Skew Correction • Limiting the range of skew angles. • Binary search for finding the maximizer of a function. • Computing the sum of pixels along parallel lines at an angle, instead of rotation at the angle. • Reducing the size of input image, as much as structure of text lines is preserved. • MIN, MAX downsampling • Local skew correction, after line segmentation, by robust line fitting.

26. Slant Correction uniform non-uniform

27. Slant Correction • The deviation of average near-vertical strokes from the vertical direction. • Occurring in handwritten and machine-printed texts. اراک • Slant is non-informative. • The average slant angle is estimated first and then a shear transformation in horizontal direction is applied to the word (or line) image to correct its slant.

28. Slant Correction • The most effective methods are based on the analysis of vertical projection profiles (histograms) at various angles. • Identical to the projection profile based methods for skew correction, except that: • The histograms are computed in vertical rather than horizontal direction. • Shear transformation is used instead of rotation. • Accurate result for handwritten words with uniform slant. • Robust to noise.

29. Slant Correction

30. Slant Correction • Projection profile technique: goodness measure

31. Slant Correction • Postprocessing: • Smoothing jagged edges. … and after smoothing after slant correction A part of a slanted word

32. Skeletonization

33. Skeletonization • Skeletonization or medial axis transform (MAT) of a shape has been one the most surveyed problems in image processing and machine vision. • A skeletonization (thinning) algorithm transforms a shape into arcs and curves of thickness one which is called skeleton. • An ideal skeleton has the following properties: • retaining basic structural properties of the original shape • well-centered • well-connected • precisely reconstructable • robust

34. Skeletonization • Simplifying classification: • Diminishing variability and distortion of instances of one class. • Reducing the amount of data to be handled. • Proved to be effective in pattern recognition problems: • Character recognition • Fingerprint recognition • Chromosome recognition • … • Providing compact representations and structural analysis of objects.

35. Skeletonization Five different skeletonization algorithms were compared and contrasted with the main focus on preserving text characteristics: • Naccache, N. J. and Shinghal, R. (1984). "SPTA: A Proposed Algorithm for Digital Pictures", IEEE Trans. on Systems, Man and Cybernetics, vol. SMC-14(3), pp. 409-418. • Zhang, T. Y. and Suen, C. Y. (1984). "A Fast Parallel Algorithm for Thinning Digital Patterns", Comm. ACM, vol. 27(3), pp. 236-239. • Ji, L. and Piper, J. (1992). "Fast Homotopy-Preserving Skeletons Using Mathematical Morphology", IEEE Trans. on PAMI, vol. 14(6), pp. 653 - 664. • Sajjadi, M. R. (Oct. 1996). "Skeletonization of Persian Characters", M. Sc. Thesis, Computer Science and Engineering Department, Shiraz University, Iran. • Huang, L., Wan, G. and Liu, C. (2003). "An Improved Parallel Thinning Algorithm", Proceedings of the Seventh International Conference on Document Analysis and Recognition (ICDAR 2003), pp. 780-783.

36. Skeletonization Homotopy-Preserving Input Zhang-Suen Huang et al. SPTA DTSA

37. Skeletonization Homotopy-Preserving Zhang-Suen Input Huang et al. SPTA DTSA

38. Skeletonization SPTA Input robustness to border noise DTSA Huang et al.

39. Skeletonization • Postprocessing: • Removing spurious branches

40. Skeletonization • Modification: • Removing 4-connectivity, and preserving 8-connectivity of the pattern. …

41. Structural Feature Extraction • The connectivity number Cn: end-point dot Cn=0 Cn=1 Cn=2 branch-point cross-point Cn=2 Cn=3 Cn=4

42. Structural Feature Extraction • Capable of tolerating much variation. • Not robust to noise. • Hard to extract. • 1D HMM needs 1D observation sequence. • Converting 2D word image into a 1D signal. • speech recognition, online handwritten recognition: 1D signal. • offline handwritten recognition: 2D signal.

43. Structural Feature Extraction • Converting the word skeleton into a graph. • Tracing the edges in a canonical order:

44. Structural Feature Extraction • Loop Extraction: • Important distinctive features. • Making the number of strokes smaller: • Easier Modeling • Lower Computational Cost • Different types of loops: • simple-loop • multi-link-loop • double-loop • A DFS algorithm was written to find complex loops in the word graph.

45. Structural Feature Extraction هـ ـهـ ـصـ ـط ـمـ ـو ـه ... صـ ص ف مـ و ...

46. Structural Feature Extraction • Each edge is transformed into a 10D feature vector: • Normalized length feature (f1) • Curvature feature (f2) • Slope feature (f3) • Connection type feature (f4) • Endpoint distance feature (f5 ) • Number of segments feature (f6 ) • Curved features (f7-f10) • Independent of the baseline location. • Invariance against scaling, translation and rotation.

47. Structural Feature Extraction 1: [0.68, 1.00, 6, 0 , 0.05, 1, 0.0, 0.0, 0.7, 0.0] 2: [0.11, 1.01, 6, 1 , 0.23, 1, 0.0, 0.0, 0.0, 0.0] 3: [2.00, 3.00, 8, 10, 0.00, 0, 0.0, 0.0, 0.0, 0.0] ...

48. Hidden Markov Models • Signal Modeling: • Deterministic • Stochastic: • Characterizing the signal by a parametric random process. • HMM is a widely used statistical (stochastic) model: • The most widely used technique in modern ASR systems. • Speech and handwritten text are similar: • Symbols with ambiguous boundaries. • Symbols with variations in appearance. • Not modeling the whole pattern as a single feature vector, exploring the relationship between consecutive segments.

49. Hidden Markov Models • Nondeterministic finite state machines: • Probabilistic state transition. • Each state is associated with a random function. • Unknown state sequence. • Some probabilistic function of the state sequence can be seen.

50. Hidden Markov Models N:The Number of states of the model S={s1, s2, ..., sN}: The set of states ∏ = {πi= P(si at t = 1)}:The initial state probabilities A = {aij = P(sj at t+1 | si at t)}:The state transition probabilities M:The Number of observation symbols V = {v1, v2, ..., vM}:The set of possible observation symbols B = {bi(vk) = P(vk at t | si at t}:The symbol emission probabilities Ot:The observed symbol at time t T:The length of observation sequence λ = (A, B, ∏):The compact notation to denote the HMM.