Method to Distinguish between Handwritten and Machine-printed Characters Inspired by Human Vision System

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1 Vol. 15, No Method to Distinguish between Handwritten and Machine-printed Characters Inspired by Human Vision System Jumpei Koyama, 1 Masahiro Kato 2 and Akira Hirose 1 Department of Electronic Engineering, The University of Tokyo 1 Fuji Xerox Co., Ltd. 2 Spectrum-domain local fluctuation detection, SDLFD SDLFD SDLFD 1. Optical character recognition, OCR OCR OCR Fan 1) Kuhnke 2) Pal Chaudhuri 3) 4, 5)

2 166 Vol. 15, No Fig. 1 Example of a local region I(x, y) in a document image and its power spectrum P (u, v). 6 8) Julesz 9) 10 12) 13, 14) 15) 16 18) 19) Spectrum-domain local fluctuation detection, SDLFD 20 22) SDLFD Multilayer perceptron, MLP 2 SDLFD 3 4 SDLFD 2. SDLFD 2.1 I(x, y) Fig. 1 w w I(x, y) (1) F (u, v)

3 167 Fig. 3 2-layer perceptron. Fig. 2 Spectrum-domain local fluctuation detection method. Every dotted rectangle is a power summation region in θ l direction. F (u, v) = w 2 1 w 2 1 x= w y= 2 w 2 exp I(x, y)h(x, y) { 2πj( x w u + y w v ) } (1) j = 1 H(x, y) F (u, v) Fig. 1 P (u, v) = F (u, v) 2 (2) 2 (u, v) Fig. 1 P (u, v) Fig. 2 θ l f l f l = v tan(θ l )u 1 P (u, v) P (u, v) u v (3) l =16 SDLFD Fig. 3 2 f l h n h 1

4 168 Vol. 15, No Table 1 Mean square error (MSE) and correct-distinction rate of the optimized multilayer perceptron in each letter type class. Letter type class Correct-distinction rate (%) MSE ( 10 2 ) Alphanumeric Hiragana Katakana Chinese character All letter type t f l h 1 t E E t Mean square error, MSE ɛ = T = 1000 Fig. 4 A document image which has randomly arranged characters. 3. SDLFD Fig. 4 Fig ETL 23) 120 w c (w, c) = (128, 64) ETL ETL1 ETL2 ETL9 5 ETL ETL

6 Relationship between the window interval d and number of correctly distinguished characters. 5 η β SDLFD 0.

5 169 Fig. 5 Binarized handwriting likelihood map with window interval d = 8. The threshold value for binarization is 0.5. Characters in dashed-line frames are misclassified. Fig. 6 Relationship between the window interval d and number of correctly distinguished characters. 5 η β SDLFD 0.5 Table 1 MSE 99.75% 97.45% 98.1% 15) SDLFD 97% d d ETL c p 64 w p 128 ETL c l w l p l processing learning Fig. 5 d = % Fig %

6 170 Vol. 15, No Table 2 Mean square error (MSE) and correct-distinction rate of the optimized multilayer perceptrons with datasets which have machine-printed character images in MS Gothic font. Letter type class Correct-distinction rate (%) MSE ( 10 2 ) Alphanumeric Hiragana Katakana Chinese All letter type Fig. 7 A sample document image. Fig. 6 d d d c p d c p Fig. 7 r w Fig. 7 MS MS ETL ETL w l 128 c l 64 Table 2 MS MSE Correct-distinction rate Table 3 r c p w p d Fig. 8 Table dpi Set A 300 dpi Set C 600 dpi Set D dpi Set A 300 dpi Set C 600 dpi Set

7 171 Table 3 Parameters of datasets A, B, C and D. Label Resolution r Character size c p Analysis window size w p Interval d Set A Set B Set C Set D (a) (b) Fig. 8 (c) (d) Binarized handwritten likelihood maps of the document images scanned in various resolutions. Threshold of binarization is 0.5. (a) Set A, (b) Set B, (c) Set C, (d) Set D. D 200 dpi Set B w p c p 150 dpi Set A 300 dpi Set C 600 dpi Set D w p c p w p /c p w l c l w l /c l dpi Set B 1.5 w p r c p w l w p (4) c l c p (4) 4. Spectrum-domain local fluctuation detection, SDLFD SDLFD

8 172 Vol. 15, No OCR SDLFD 1) Fan, K.C., Wang, L.S., and Tu, Y.T. (1998): Classification of machine-printed and handwritten texts using character block layout variance, Pattern Recognition, Vol.31, No.9, pp ) Kuhnke, K., Simoncini, L., and Kovács-V, Zs. M. (1995): A system for machine-written and hand-written character distinction, Proc. IEEE Int. Conf. on Document Analysis and Recognition, Vol.2, pp ) Pal, U. and Chaudhuri, B.B. (2001): Machine-printed and hand-written text lines identification, Pattern Recognition Letters, Vol.22, pp ) Khoubyari, S. and Hull, J.J. (1996): Font and function word identification in document recognition, Computer Vision and Image Understanding, Vol.63, No.1, pp ) Zramdini, A. and Ingold, R. (1998): Optical font recognition using typographical features, IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.20, No.8, pp ) Nothdurft, H.C. (1985): Sensitivity for structure gradient in texture discrimination tasks, Vision Research, Vol.25, No.12, pp ) Kingdom, F.A.A., Keeble, D., and Moulden, B. (1995): Sensitivity to orientation modulation in micropattern-based textures, Vision Research, Vol.35, pp ) Motoyoshi, I. and Nishida, S. (2002): Spatiotemporal interactions in detection of texture orientation modulations, Vision Research, Vol.42, pp ) Julesz, B. (1965): Texture and visual perception, Scientific American, Vol.212, pp ) Papathomas, T.V., Kashi, R.S., and Gorea, A. (1997): A human vision based computational model for chromatic texture segregation, IEEE Trans. on Systems, Man, and Cybernetics Part B: Cybernetics, Vol.27, No.3, pp ) Wouwer, G.V., Scheunders, P., and Dyck, D.V. (1999): Statistical texture characterization from discrete wavelet representations, IEEE Trans. on Image Processing, Vol.8, No.4, pp ) Han, J. and Ma, K.K. (2007): Rotationinvariant and scale-invariant Gabor features for texture image retrieval, Image and Vision Computing, Vol.25, pp ) Jones, J.P. and Palmer, L. (1987): An evaluation of the two-dimensional Gabor filter model of simple receptive fields in cat striate cortex, J. Neurophysiology, Vol.58, No.6, pp ) Daugman, J.G. (1985): Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters, J. Opt. Soc. Am. A, Vol.2, No.7, pp ) Zheng, Y., Li, H., and Doermann, D. (2004): Machine printed text and handwriting identification in noisy document images, IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.26, No.3, pp

9 173 16) Zhu, Y., Tan, T., and Wang, Y. (2001): Font recognition based on global texture analysis, IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.23, No.10, pp ) Tan, T.N. (1998): Rotation invariant texture features and their use in automatic script identification, IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.20, No.7, pp ) Morris, R.A. (1992): Classification of digital typefaces using spectral signatures, Pattern Recognition, Vol.25, No.8, pp ) Busch, A., Boles, W.W., and Sridharan, S. (2005): Texture for script identification, IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.27, No.11, pp ) Koyama, J., Kato, M., and Hirose, A. (2007): Handwritten character distinction method inspired by human vision mechanism, Proc. Int. Conf. on Neural Information Processing (ICONIP) 2007 Kitakyushu, FMF-5 21) Koyama, J., Kato, M., and Hirose, A. (2008): Distinction between handwritten and machine-printed characters without extracting characters or text lines, Proc. IEEE World Congress on Computational Intelligence (WCCI) 2008 Hong Kong, NN ) Koyama, J., Kato, M., and Hirose, A. (2008): Local-spectrum-based distinction between handwritten and machine-printed characters, Proc. IEEE Int. Conf. on Image Processing (ICIP) 2008 San Diego (to be presented) 23) ETL character databases, Advanced industrial science and technology,

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