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Beyond OCR: Handwritten manuscript attribute understanding

He, Sheng

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2017

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He, S. (2017). Beyond OCR: Handwritten manuscript attribute understanding. University of Groningen.

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Bibliography

Abdi, M. N. and Khemakhem, M.: 2015, A model-based approach to offline text-independent Arabic writer identification and verification, Pattern Recognition48(5), 1890–1903.

Arabadjis, D., Giannopoulos, F., Papaodysseus, C., Zannos, S., Rousopoulos, P., Panagopoulos, M. and Blackwell, C.: 2013, New mathematical and algorithmic schemes for pattern classification with application to the identification of writers of important ancient documents, Pattern Recognition 46(8), 2278–2296.

Arazi, B.: 1977, Handwriting identification by means of run-length measurements, IEEE Trans. Syst., Man and Cybernetics(12), 878–881.

Arbelaez, P., Maire, M., Fowlkes, C. and Malik, J.: 2011, Contour detection and hierarchical image segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence33(5), 898–916. Belongie, S., Malik, J. and Puzicha, J.: 2002, Shape matching and object recognition using shape

contexts, IEEE Transactions on Pattern Analysis and Machine Intelligence24(4), 509–522. Benhamou, S.: 2004, How to reliably estimate the tortuosity of an animal’s path: straightness,

sinuos-ity, or fractal dimension?, Journal of theoretical biology229(2), 209–220.

Brink, A., Smit, J., Bulacu, M. and Schomaker, L.: 2012, Writer identification using directional ink-trace width measurements, Pattern Recognition45(1), 162–171.

Bulacu, M. and Schomaker, L.: 2003, Writer style from oriented edge fragments, Computer Analysis of Images and Patterns, Springer, pp. 460–469.

Bulacu, M. and Schomaker, L.: 2005, A comparison of clustering methods for writer identification and verification, International Conference on Document Analysis and Recognition (ICDAR), pp. 1275– 1279.

Bulacu, M. and Schomaker, L.: 2007, Text-independent writer identification and verification using textural and allographic features, IEEE Transactions on Pattern Analysis and Machine Intelligence 29(4), 701–717.

(3)

Bulacu, M., Schomaker, L. and Brink, A.: 2007, Text-independent writer identification and verification on offline Arabic handwriting, International Conference on Document Analysis and Recognition (ICDAR), Vol. 2, pp. 769–773.

Bulacu, M., Schomaker, L. et al.: 2006, Combining multiple features for text-independent writer iden-tification and verification, Tenth International Workshop on Frontiers in Handwriting Recognition. Busch, A., Boles, W. W. and Sridharan, S.: 2005, Texture for script identification, IEEE Transactions

on Pattern Analysis and Machine Intelligence27(11), 1720–1732.

Chang, C.-C. and Lin, C.-J.: 2011, LIBSVM: a library for support vector machines, ACM Transactions on Intelligent Systems and Technology (TIST)2(3), 27.

Csurka, G., Dance, C., Fan, L., Willamowski, J. and Bray, C.: 2004, Visual categorization with bags of keypoints, Workshop on statistical learning in computer vision, ECCV.

Dalal, N. and Triggs, B.: 2005, Histograms of oriented gradients for human detection, International Conference on Computer Vision and Pattern Recognition (CVPR), Vol. 1, pp. 886–893.

Deng, J., Krause, J. and Fei-Fei, L.: 2013, Fine-grained crowdsourcing for fine-grained recognition, Conference on Computer Vision and Pattern Recognition (CVPR), pp. 580–587.

Djeddi, C., Siddiqi, I., Souici-Meslati, L. and Ennaji, A.: 2013, Text-independent writer recognition using multi-script handwritten texts, Pattern Recognition Letters34(10), 1196–1202.

Doersch, C., Singh, S., Gupta, A., Sivic, J. and Efros, A.: 2012, What makes Paris look like Paris?, ACM Transactions on Graphics31(4).

Doll´ar, P. and Zitnick, C. L.: 2013, Structured forests for fast edge detection, International Conference on Computer Vision (ICCV), pp. 1841–1848.

Epshtein, B., Ofek, E. and Wexler, Y.: 2010, Detecting text in natural scenes with stroke width trans-form, Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2963–2970.

Fan, K.-C. and Wu, W.-H.: 2000, A run-length-coding-based approach to stroke extraction of Chinese characters, Pattern Recognition33(11), 1881–1895.

Fei-Fei, L. and Perona, P.: 2005, A bayesian hierarchical model for learning natural scene categories, Conference on Computer Vision and Pattern Recognition (CVPR), Vol. 2, pp. 524–531.

Ferrari, V., Fevrier, L., Jurie, F. and Schmid, C.: 2008, Groups of Adjacent Contour Segments for Object Detection, IEEE Transactions on Pattern Analysis and Machine Intelligence30(1), 36–51. Ferrari, V., Tuytelaars, T. and Van Gool, L.: 2006, Object detection by contour segment networks,

Computer Vision–ECCV, pp. 14–28.

Geng, X., Zhou, Z.-H. and Smith-Miles, K.: 2007, Automatic age estimation based on facial aging patterns, IEEE Transactions on Pattern Analysis and Machine Intelligence29(12), 2234–2240. Ghiasi, G. and Safabakhsh, R.: 2010, An efficient method for offline text independent writer

(4)

BIBLIOGRAPHY 151

Ghiasi, G. and Safabakhsh, R.: 2013, Offline text-independent writer identification using codebook and efficient code extraction methods, Image and Vision Computing31(5), 379–391.

Ghosh, D., Dube, T. and Shivaprasad, A. P.: 2010, Script recognition: A review, IEEE Transactions on Pattern Analysis and Machine Intelligence32(12), 2142–2161.

Gordo, A., Forn´es, A. and Valveny, E.: 2013, Writer identification in handwritten musical scores with bags of notes, Pattern Recognition46(5), 1337–1345.

Gordo, A., Perronnin, F. and Valveny, E.: 2013, Large-scale document image retrieval and classification with runlength histograms and binary embeddings, Pattern Recognition46(7), 1898–1905. Guerfali, W. and Plamondon, R.: 1998, A new method for the analysis of simple and complex planar

rapid movements, Journal of neuroscience methods82(1), 35–45.

Guo, G., Fu, Y., Dyer, C. R. and Huang, T. S.: 2008, Image-based human age estimation by man-ifold learning and locally adjusted robust regression, IEEE Transactions on Image Processing 17(7), 1178–1188.

Hannad, Y., Siddiqi, I. and El Kettani, M. E. Y.: 2016, Writer identification using texture descriptors of handwritten fragments, Expert Systems with Applications47, 14–22.

Haralick, R. M., Shanmugam, K. and Dinstein, I. H.: 1973, Textural features for image classification, IEEE Transactions on Systems, Man and Cybernetics(6), 610–621.

Hassaine, A. and Maadeed, S. A.: 2012, ICFHR 2012 competition on writer identification challenge 2: Arabic scripts, International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 835–840.

He, K., Sun, J. and Tang, X.: 2013, Guided image filtering, IEEE Transactions on Pattern Analysis and Machine Intelligence35(6), 1397–1409.

He, S., Samara, P., Burgers, J. and Schomaker, L.: 2014, Towards style-based dating of historical doc-uments, International Conference on Frontiers in Handwriting Recognition (ICFHR2014), pp. 265– 270.

He, S. and Schomaker, L.: 2015, A polar stroke descriptor for classification of historical documents, International Conference on Document Analysis and Recognition (ICDAR), pp. 6–10.

Hearn, D. and Baker, M. P.: 1997, Computer graphics, C version, Vol. 2, Prentice Hall Upper Saddle River.

Helli, B. and Moghaddam, M. E.: 2010, A text-independent Persian writer identification based on feature relation graph (FRG), Pattern Recognition43(6), 2199–2209.

Huang, D., Zhu, C., Wang, Y. and Chen, L.: 2014, HSOG: a novel local image descriptor based on histograms of the second-order gradients, IEEE Transactions on Image Processing23(11), 4680– 4695.

Huber, R. A. and Headrick, A. M.: 1999, Handwriting identification: facts and fundamentals, CRC press.

(5)

Ito, S. and Kubota, S.: 2010, Object classification using heterogeneous co-occurrence features, Com-puter Vision–ECCV, pp. 701–714.

Jain, R. and Doermann, D.: 2011, Offline writer identification using k-adjacent segments, International Conference on Document Analysis and Recognition (ICDAR), pp. 769–773.

Javed, M., Nagabhushan, P. and Chaudhuri, B.: 2015, Automatic extraction of correlation-entropy fea-tures for text document analysis directly in run-length compressed domain, International Conference on Document Analysis and Recognition (ICDAR), pp. 1–5.

Jevnisek, R. J. and Avidan, S.: 2016, Semi global boundary detection, Computer Vision and Image Understanding152, 21–28.

Juneja, M., Vedaldi, A., Jawahar, C. and Zisserman, A.: 2013, Blocks that shout: Distinctive parts for scene classification, International Conference on Computer Vision and Pattern Recognition (CVPR), pp. 923–930.

Kato, Y. and Yasuhara, M.: 2000, Recovery of drawing order from single-stroke handwriting images, IEEE Transactions on Pattern Analysis and Machine Intelligence22(9), 938–949.

Kholmatov, A. and Yanikoglu, B.: 2005, Identity authentication using improved online signature veri-fication method, Pattern Recognition Letters26(15), 2400–2408.

Kleber, F., Fiel, S., Diem, M. and Sablatnig, R.: 2013, CVL-database: An off-line database for writer retrieval, writer identification and word spotting, International Conference on Document Analysis and Recognition (ICDAR), pp. 560–564.

Kohonen, T.: 1988, Self-organization and associative memory, Springer Verlag1.

Lampert, C. H., Nickisch, H. and Harmeling, S.: 2014, Attribute-based classification for zero-shot visual object categorization, IEEE Transactions on Pattern Analysis and Machine Intelligence 36(3), 453–465.

Latecki, L. J. and Lak¨amper, R.: 1999, Convexity rule for shape decomposition based on discrete contour evolution, Computer Vision and Image Understanding73(3), 441–454.

Lee, Y. J., Efros, A. A. and Hebert, M.: 2013, Style-aware mid-level representation for discover-ing visual connections in space and time, International Conference on Computer Vision (ICCV), pp. 1857–1864.

Li, Y., Genzel, D., Fujii, Y. and C.Popat, A.: 2015, Publication date estimation for printed histori-cal documents using convolutional neural networks, Workshop on Historihistori-cal Document Image and Processing (HIP), pp. 99–106.

Liu, K., Huang, Y. S. and Suen, C. Y.: 1999, Identification of fork points on the skeletons of handwritten Chinese characters, IEEE Transactions on Pattern Analysis and Machine Intelligence21(10), 1095– 1100.

Lorigo, L. M. and Govindaraju, V.: 2006, Offline Arabic handwriting recognition: a survey, IEEE Transactions on Pattern Analysis and Machine Intelligence28(5), 712–724.

(6)

BIBLIOGRAPHY 153

Louloudis, G., Gatos, B., Stamatopoulos, N. and Papandreou, A.: 2013, ICDAR 2013 competition on writer identification, International Conference on Document Analysis and Recognition (ICDAR), IEEE, pp. 1397–1401.

Lowe, D. G.: 2004, Distinctive image features from scale-invariant keypoints, International journal of computer vision60(2), 91–110.

Maire, M., Arbel´aez, P., Fowlkes, C. and Malik, J.: 2008, Using contours to detect and localize junc-tions in natural images, Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–8. Marinai, S., Miotti, B. and Soda, G.: 2010, Bag of characters and SOM clustering for script recognition and writer identification, International Conference on Pattern Recognition (ICPR), pp. 2182–2185. Marti, U.-V. and Bunke, H.: 2002, The IAM-database: an English sentence database for offline

hand-writing recognition, International Journal on Document Analysis and Recognition5(1), 39–46. Martin, D. R., Fowlkes, C. C. and Malik, J.: 2004, Learning to detect natural image boundaries

us-ing local brightness, color, and texture cues, IEEE Transactions on Pattern Analysis and Machine Intelligence26(5), 530–549.

Meijster, A., Roerdink, J. B. and Hesselink, W. H.: 2000, A general algorithm for computing dis-tance transforms in linear time, Mathematical Morphology and its applications to image and signal processing, Springer, pp. 331–340.

Mensink, T., Gavves, E. and Snoek, C. G.: 2014, COSTA: Co-occurrence statistics for zero-shot clas-sification, Computer Vision and Pattern Recognition (CVPR), pp. 2441–2448.

Meulenbroek, R. G. and Van Galen, G. P.: 1988, The acquisition of skilled handwriting: Discontinuous trends in kinematic variables, Advances in psychology55, 273–281.

Mikolajczyk, K. and Schmid, C.: 2005, A performance evaluation of local descriptors, IEEE Transac-tions on Pattern Analysis and Machine Intelligence27(10), 1615–1630.

Moghaddam, R. F. and Cheriet, M.: 2012, AdOtsu: an adaptive and parameterless generalization of Otsu’s method for document image binarization, Pattern Recognition45(6), 2419–2431.

Morasso, P. and Ivaldi, F. M.: 1982, Trajectory formation and handwriting: a computational model, Biological cybernetics45(2), 131–142.

Morris, R. and Morris, R. N.: 2000, Forensic handwriting identification: fundamental concepts and principles, Academic press.

Neubeck, A. and Van Gool, L.: 2006, Efficient non-maximum suppression, International Conference on Pattern Recognition (ICPR), Vol. 3, pp. 850–855.

Newell, A. J. and Griffin, L. D.: 2014, Writer identification using oriented basic image features and the delta encoding, Pattern Recognition47(6), 2255–2265.

Ojala, T., Pietik¨ainen, M. and M¨aenp¨a¨a, T.: 2002, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns, IEEE Transactions on Pattern Analysis and Machine Intelligence24(7), 971–987.

(7)

Otsu, N.: 1975, A threshold selection method from gray-level histograms, Automatica 11(285-296), 23–27.

Palermo, F., Hays, J. and Efros, A. A.: 2012, Dating historical color images, Computer Vision–ECCV, pp. 499–512.

Panagopoulos, M., Papaodysseus, C., Rousopoulos, P., Dafi, D. and Tracy, S.: 2009, Automatic writer identification of ancient greek inscriptions, IEEE transactions on pattern analysis and machine in-telligence31(8), 1404–1414.

Parida, L., Geiger, D. and Hummel, R.: 1998, Junctions: Detection, classification, and reconstruction, IEEE Transactions on Pattern Analysis and Machine Intelligence20(7), 687–698.

Parikh, D. and Grauman, K.: 2011, Relative attributes, International Conference on Computer Vision (ICCV), pp. 503–510.

Parvez, M. T. and Mahmoud, S. A.: 2013, Arabic handwriting recognition using structural and syntactic pattern attributes, Pattern Recognition46(1), 141–154.

Pavlidis, T. and Zhou, J.: 1992, Page segmentation and classification, CVGIP: Graphical models and image processing54(6), 484–496.

Pervouchine, V. and Leedham, G.: 2007, Extraction and analysis of forensic document examiner fea-tures used for writer identification, Pattern Recognition40(3), 1004–1013.

Pham, T.-A., Delalandre, M., Barrat, S. and Ramel, J.-Y.: 2014, Accurate junction detection and char-acterization in line-drawing images, Pattern Recognition47(1), 282–295.

Prasad, D. K., Quek, C., Leung, M. K. and Cho, S.-Y.: 2011, A parameter independent line fitting method, Asian Conference on Pattern Recognition (ACPR2011), pp. 441–445.

Qi, X., Xiao, R., Li, C.-G., Qiao, Y., Guo, J. and Tang, X.: 2014, Pairwise rotation invariant co-occurrence local binary pattern, IEEE Transactions on Pattern Analysis and Machine Intelligence 36(11), 2199–2213.

R.Howe, N., Yang, A. and Penn, M.: 2015, A character style library for Syriac manuscripts, Workshop on Historical Document Image and Processing (HIP), pp. 123–128.

Richiardi, J., Ketabdar, H. and Drygajlo, A.: 2005, Local and global feature selection for on-line signature verification, Proceedings. Eighth International Conference on Document Analysis and Recognition (ICDAR)., pp. 625–629.

Roman-Rangel, E., Pallan, C., Odobez, J.-M. and Gatica-Perez, D.: 2011, Analyzing ancient Maya glyph collections with contextual shape descriptors, International Journal of Computer Vision 94(1), 101–117.

Rusi˜nol, M., Aldavert, D., Toledo, R. and Llad´os, J.: 2015, Efficient segmentationfree keyword spotting in historical document collections, Pattern Recognition48(2), 545–555.

Rusi˜nol, M. and Llad´os, J.: 2014, Boosting the handwritten word spotting experience by including the user in the loop, Pattern Recognition47(3), 1063–1072.

(8)

BIBLIOGRAPHY 155

Russakovsky, O. and Fei-Fei, L.: 2010, Attribute learning in large-scale datasets, European Conference on Computer Vision (ECCV), pp. 1–14.

Said, H. E., Tan, T. N. and Baker, K. D.: 2000, Personal identification based on handwriting, Pattern Recognition33(1), 149–160.

Schomaker, L.: 1993, Using stroke-or character-based self-organizing maps in the recognition of on-line, connected cursive script, Pattern Recognition26(3), 443–450.

Schomaker, L. and Bulacu, M.: 2004, Automatic writer identification using connected-component con-tours and edge-based features of uppercase western script, IEEE Transactions on Pattern Analysis and Machine Intelligence26(4), 787–798.

Schomaker, L., Franke, K. and Bulacu, M.: 2007, Using codebooks of fragmented connected-component contours in forensic and historic writer identification, Pattern Recognition Letters 28(6), 719–727.

Schomaker, L. R. and Teulings, H.-L.: 1990, A handwriting recognition system based on properties of the human motor system, International Workshop on Frontiers in Handwriting Recognition, Mon-treal, April.

Schomaker, L., Thomassen, A. and Teulings, H.: 1989, A computational model of cursive handwriting, Computer recognition and human production of handwritingpp. 153–177.

Schomaker, L. and Vuurpijl, L.: 2000, Forensic writer identificaiton: a benchmark data set and a comparison of two systems, Technical Report .

Shababi, F. and Rahmati, M.: 2009, A new method for writer identification of handwritten Farsi docu-ments, International Conference on Document Analysis and Recognition (ICDAR), pp. 426–430. Shechtman, E. and Irani, M.: 2007, Matching local self-similarities across images and videos,

Interna-tional Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–8.

Shi, B., Bai, X. and Yao, C.: 2016, Script identification in the wild via discriminative convolutional neural network, Pattern Recognition52, 448–458.

Shijian, L. and Tan, C. L.: 2008, Script and language identification in noisy and degraded document images, IEEE Transactions on Pattern Analysis and Machine Intelligence30(1), 14–24.

Siddiqi, I. and Vincent, N.: 2010, Text independent writer recognition using redundant writing patterns with contour-based orientation and curvature features, Pattern Recognition43(11), 3853–3865. Simon, J.-C. and Baret, O.: 1991, Regularities and singularities in line pictures, International Journal

of Pattern Recognition and Artificial Intelligence5, 57–77.

Singh, S., Gupta, A. and Efros, A.: 2012, Unsupervised discovery of mid-level discriminative patches, Computer Vision–ECCVpp. 73–86.

Sinzinger, E. D.: 2008, A model-based approach to junction detection using radial energy, Pattern Recognition41(2), 494–505.

(9)

Stokes, P. A.: 2015, Digital approaches to paleography and book history: Some challenges, present and future, Frontiers in Digital Humanities2, 5.

Su, R., Sun, C. and Pham, T. D.: 2012, Junction detection for linear structures based on Hessian, correlation and shape information, Pattern Recognition45(10), 3695–3706.

Teulings, H.-L. and Maarse, F. J.: 1984, Digital recording and processing of handwriting movements, Human Movement Science3(1), 193–217.

Teulings, H.-L., Thomassen, A. J. and van Galen, G. P.: 1986, Invariants in handwriting: The informa-tion contained in a motor program, Advances in Psychology37, 305–315.

Tola, E., Lepetit, V. and Fua, P.: 2010, Daisy: An efficient dense descriptor applied to wide-baseline stereo, IEEE Transactions on Pattern Analysis and Machine Intelligence32(5), 815–830.

Van der Zant, T., Schomaker, L. and Haak, K.: 2008, Handwritten-word spotting using biologically inspired features, IEEE Transactions on Pattern Analysis and Machine Intelligence30(11), 1945– 1957.

Van Oosten, J.-P. and Schomaker, L.: 2014, Separability versus prototypicality in handwritten word-image retrieval, Pattern Recognition47(3), 1031–1038.

Vincent, L.: 2007, Google book search: Document understanding on a massive scale, International Conference on Document Analysis and Recognition (ICDAR), Vol. 2, pp. 819–823.

Von Gioi, R. G., Jakubowicz, J., Morel, J.-M. and Randall, G.: 2010, LSD: A fast line segment detector with a false detection control, IEEE Transactions on Pattern Analysis and Machine Intelligence 32(4), 722–732.

Wahlberg, F., Martensson, L. and Brun, A.: 2015, Large scale style based dating of medieval manuscripts, Workshop on Historical Document Image and Processing (HIP), pp. 107–114. Wang, B., Bai, X., Wang, X., Liu, W. and Tu, Z.: 2010, Object recognition using junctions, Computer

Vision–ECCV, Springer, pp. 15–28.

Wang, J., Yang, J., Yu, K., Lv, F., Huang, T. and Gong, Y.: 2010, Locality-constrained linear coding for image classification, Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3360– 3367.

Wang, X., Feng, B., Bai, X., Liu, W. and Latecki, L. J.: 2014, Bag of contour fragments for robust shape classification, Pattern Recognition47(6), 2116–2125.

Wu, X., Tang, Y. and Bu, W.: 2014, Offline text-independent writer identification based on scale invariant feature transform, IEEE Transactions on Information Forensics and Security9(3), 526– 536.

Xia, G.-S., Delon, J. and Gousseau, Y.: 2014, Accurate junction detection and characterization in natural images, International Journal of Computer Vision106(1), 31–56.

Yang, S., Chen, M., Pomerleau, D. and Sukthankar, R.: 2010, Food recognition using statistics of pairwise local features, Computer Vision and Pattern Recognition, pp. 2249–2256.

(10)

BIBLIOGRAPHY 157

Zhang, X. and Tan, C. L.: 2014, Handwritten word image matching based on heat kernel signature, Pattern Recognition48(11), 3346–3356.

Zhu, G., Yu, X., Li, Y. and Doermann, D.: 2009, Language identification for handwritten document images using a shape codebook, Pattern Recognition42(12), 3184–3191.

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