NEW ALGORITHMS FOR SKEWING CORRECTION AND SLANT REMOVAL ON WORD-LEVEL

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1 NEW ALGORITHMS FOR SKEWING CORRECTION AND SLANT REMOVAL ON WORD-LEVEL E.Kavallieratou N.Fakotakis G.Kokkinakis Wire Communication Laboratory, University of Patras, Patras, ABSTRACT The word preprocessing is a crucial stage of OCR systems. Here we present two algorithms appropriate for this stage. The first one corrects the skewing of words and the second removes the slant from handwritten words. Both algorithms make use of the Wigner-Ville Distribution and the projection profile technique. The algorithms have been tested on words taken from more than 200 writers and the results obtained can be considered very satisfactory, since the overall accuracy of our OCR system is notably improved. 1. INTRODUCTION A major problem of Pattern Recognition is the Optical Character Recognition (OCR). Numerous systems have been proposed during the last decades. Nowadays, the recognition of printed (Fig.1a) and hand-printed characters (Fig.1b) is performed with accuracy that exceeds 90%[1]. However, the recognition of script writing (Fig.1c) still remains an open problem since the existence of connections between successive letters as well as the possibility to be slanted, may distort the characters giving them unexpected shapes. Various approaches have been presented which deal with the recognition of cursive script words. Some work by segmenting the word and then performing character recognition [2] [3], other by avoiding the segmentation stage [4][5], while other even claim that the segmentation introduces an error [6][7]. However, whatever the application and the methodology is, the preprocessing stage is necessary both for the segmentation procedure as well as for the character recognition, since skewed or slanted words (Fig.1d,e) may cause considerable difficulties in both tasks. In this paper we present two algorithms that deal with these crucial preprocessing problems. The first algorithm corrects the skewing of printed or handwritten words while the second removes the slant from a handwritten word. Both methods are based on the Wigner-Ville Distribution (WVD). (a) (b) (c) (d) (e) Fig.1: Examples of (a) printed (b) hand-printed (c)cursive (d) skewed and (e) slanted words. In the following section we present in brief the Wigner-Ville Distribution. The proposed algorithms are analyzed in sections 3 and 4, while the experimental results and our conclusions are given in section 5 and 6 respectively. 2.THE WIGNER-VILLE DISTRIBUTION The representation of time-varying or nonstationary signals, that is signals the characteristics of which vary with time, is a very important chapter of signal processing. For such signals the concept of time-frequency distributions has been introduced. One of the most employed distributions that succeeds

2 a joint function of time and frequency is the Wigner- Ville Distribution (WVD). The WVD of signal s(t) is defined as follows [8]: ( ) 2 πfτ W t, f = z( t + τ / 2) z *( t τ / 2) e dτ, (2.1) where z(t) represents the analytic signal associated with s(t). The WVD can also be expressed using the spectrum of the signal under analysis as follows: j2π ut W( t, f ) = Z( f + u / 2) Z *( f u / 2) e du. (2.2) The WVD should always be used in conjunction with the analytic signal for an effective timefrequency analysis. To do this, consider the WD defined by j2 πfτ Ws ( t, f ) = s( t + τ / 2) s * ( t τ / 2) e dτ, (2.3) where s(t) is the real signal to be analyzed. This distribution differs from the Wigner-Ville distribution (2.1) by its use of the real signal, s(t), instead of the analytic signal, z(t). The relation between the two distributions is: 1 Ws ( t, f ) = [ Wz ( t, f ) + Wz ( t, f )] + γ ( t, f ), (2.4) 4 where γ ( t, f ) represents the interaction terms between positive and negative frequencies. The discrete time analytic signal, z(n), associated with the real discrete time signal, s(n), is given by [ ] z( n) = s( n) + jη s( n), (2.5) where H[ ] is the discrete-time Hilbert transform defined by [ ( )] H s n 2s( n m) =, mπ m= (m odd). (2.6) The numerous properties of WVD make it useful in many scientific fields, such as in Pattern Recognition[8][9], Synthesis[10], Seismology[11], Optics[12]. 3. SKEWED WORDS CORRECTION The majority of both segmentation and character recognition algorithms are sensitive to the orientation of the word. Furthermore, the skewed words are very often found in handwritten text. Even in the case of correctly oriented pages, the handwritten words could present smaller or larger skews. In our approach, in order to correct a skewed word we make use of the horizontal projection of the word and the WVD of the projection. Since the envelope of the histogram is getting smoother when the word tends to be oriented at the vertical position (±90 degrees), and presents most peaks at the horizontal position (0 and 180 degrees), the corresponding WVD presents maximum intensity in the latter case. In Fig.2 a skewed word is presented at various skew angles plus the corresponding histograms, while Fig.3 shows the peak values of the maximum intensity of the WVDs with respect to the angles of the corresponding histograms. As expected, this curve presents a maximum at 0 o and 180 o where the word is located at the horizontal position, i.e. it is up right or reversed. ),' )'' (,' ('',' $ % & ' ' (' )' *' +',' -'.' $ % & Fig.2: A skewed words at various skew angles and the corresponding histograms.

3 C B A C?@ B?@ A?@ D E F G = / : 9 ; < < Fig.3: The peak values of the maximum intensity of the WVDs with respect to the angles of the corresponding histograms. The analytical algorithm is presented in Fig.4. The word is rotated from -89 to +89 degrees and the horizontal histograms are extracted for each position. The space of ±89 degrees has been selected in order to avoid to orient a word at reverse side. The WVDs are, then, calculated and the maximum intensity of each histogramis marked. Finally, the angle, whose projection presents the maximum intensity, is selected as the most appropriate and the word is corrected. Extraction of the horizontal histograms for the angles -89 o to +89 o Calculation of WVDs Selection of the angle whose WVD presents the maximum intensity Correction of the word Fig.4: The skew correction algorithm. 4. SLANT REMOVAL ALGORITHM The slanted words may cause a lot of difficulties in OCR. Especially, the segmentation of script, if required, is a very hard task in the case of slanted words. Moreover, the character recognition systems need excessive training in order to include slanted characters. The idea of our slant removal algorithm is similar to that of the skew correction algorithm. However, we make use of the vertical projection instead of the horizontal. Each word is handled as a matrix of h lines (height of word) and w columns (width of word). Extraction of the vertical histograms for slants -45 o to +45 o Calculation of WVDs Selection of the angle whose WVD presents the maximum intensity Correction of the word Fig.5: The slant removal algorithm. This matrix is separated into horizontal areas. The amount of areas increases gradually from 1 to h. Each area is slided to the right from 1 to h pixels so that a right-slanted word is formed (positive slant). In the same way, we form left-slanted words by sliding the areas to the left (negative slant). The limit of slant is reached when each area includes only one line and the upper line has been slided by h pixels. In this case the word has been slanted to the left or to the right by an angle H : tan H = h/h H = 45 o. Similarly to the skew correction algorithm, the whole idea is based on the fact that the intensity of the histogram increases when the slant of the word decreases. Thus, the graph of the maximum intensities of the WVDs of the histograms will present a maximum at the angle of minimum slant. The algorithm is presented in Fig.5. The slant of the word is changed within the area ±45 o. The vertical projection is calculated in each case and afterwards the corresponding WVD. Finally, the angle the projection of which corresponds to the maximum intensity is selected as the most appropriate and the slant is removed.

4 5. EXPERIMENTAL RESULTS Both algorithms have been tested with more than two hundred Greek and English words taken from different writers. Moreover, the skew correction algorithm has also been tested on printed words. are shown in Fig 7. However, problems may show up in the case of words with distinct slanted characters. In these cases, the word is corrected according to the slant of the highest character. The application of the algorithms to our OCR system improved the results about 1.5% in the case of hand-printed text while the improvement reaches the 3% for cursive script. Fig.6: Examples of words after the skew correction. The skew correction algorithm has dealt successfully with every word printed or handwritten with skewing angles in the area of ±89 o. Some results are presented in Fig 6. However, problems may appear in the case of words with two characters. In this case the algorithm is unable to discriminate the difference between height and width. Thus, if the word is skewed we cannot say for sure if it is rotated at 0 o or 90 o. On the other hand, if the text is very close to the correct angle, the possibility for the short words to be spoiled is minimized since the skewing angle is very small in a word of two characters. Fig.7: Examples of words after the slant removal. The slant removal algorithm deals successfully with both handprinted and script words. Some results 6. CONCLUSION We presented two algorithms appropriate for the preprocessing stage of OCR systems. The one corrects the skew angle of words, while the other removes the slant from handwritten words, if it exists. Both algorithms are based on the same idea and they make use of WVD and the projection profile of the word; the former the horizontal projection, the latter the vertical one. The algorithms should be employed in an OCR system immediately after the word segmentation procedure and before the character segmentation stage, if it exists, or before the feature extraction stage if character segmentation is skipped. They could also be used in of a character segmentation system as a preprocessing stage. In both cases, the use of the algorithms improves the accuracy and simplifies the procedure. The accuracy of the algorithms is quite satisfactory and they can easily be adapted to any system. REFERENCES [1] S.Mori, C.Suen and K.Yamamoto, Historical review of OCR research and development, Proc. IEEE, vol.80, n.7, [2] M.Y.Chen, A.Kundu, J.Zhou and S.N.Srihari, Off-line handwritten word recognition using HMM, U.S. Postal Service, 5 th Adv. Technol. Conf., pp , Washington, DC, [3] R.M.Bozinovic and S.N.Srihari, Off-line Cursive Script Word Recognition, IEEE Trans on PAMI, vol.11, n.1, pp.68-83, [4] M.Mohamed and P.Gader, Handwritten word recognition using segmentation-free hidden Markov modeling and segmentation-based dynamic programming techniques, IEEE Trans. On PAMI, vol.19, n.5, May [5] A.Dutta, An experimental procedure for handwritten character recognition, IEEE Trans. Comput., vol. C-23, pp , May 1974.

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