State of the art of writer identification
Keywords:
online and offline writer identification systems, handwritten text database, handwritten text pre-processing, binarization, line segmentation, word segmentation, feature extraction, writer identificationAbstract
The paper analytically reviews the methods and algorithms for solving problems arising in the development of writer identification offline systems. The main applied problems that are solved on the basis of the processing and analysis of handwritten text are considered; the classifications of writer identification systems, as well as the structure of offline systems are given. The paper also reviews handwritten text image databases and shows the tasks to which these databases are aimed. An attempt is made to systematize the algorithms for the preliminary processing of handwritten text images, depending on the task they are tackling at the stage under consideration. Based on the analysis of the most common algorithms of forming the handwritten text feature space, these algorithms are classified into ones for extracting geometric, structural, topological, statistical, and spectral features. A review of the algorithms for selecting each category from the listed features is conducted. Recognition methods used to identify the writer are also reviewed. The results of applying these methods, as well as their benefits and drawbacks are presented.
References
Srihari S. N. Handwritten address interpretation: a task of many pattern recognition problems. Int. Journal of Pattern Recognition and Artificial Intelligence, 14(05), 2000, pp. 663-674.
Charfi M., Kherallah M., El Baati A., Alimi A.M. A New Approach for Arabic Handwritten Postal Addresses Recognition.Int. Journal of
Advanced Computer Science and Applications, Vol. 3, No.3, 2012, pp. 1-7.
Palacios R., Gupta A. A system for processing handwritten bank checks automatically. Image and Vision Computing, Vol.26 (10), 2008, pp. 1297-1313.
Ishenko Е.P., Toporkov А.А. Forensics. INFRA-M, Moscow, 2010.
Gonzalez R.C., Woods R.E.Digital image processing. Pearson, New Jersey, 2008.
ShapiroL., StockmanG. Computer vision. Pearson, New Jersey, 2001.
Solomevich V.I. Handwriting and character. Harvest, Minsk, 2009.
Patel M., Thakkar S.P. Handwritten Character Recognition in English: A Survey. Int. Journal of Advanced Research in Computer and Communication Engineering, Vol. 4, Issue 2, 2015, pp. 345-350.
Plamondon R., Srihari S.N. Online and off-line handwriting recognition: a comprehensive survey. Pattern Analysis and Machine Intelligence, IEEE Transactions, Vol. 22, Issue 1, 2000, pp. 63-84.
Siddiqi I., Djeddi C., Raza A., Souici-meslati L. Automatic analysis of handwriting for gender classification. Pattern Analysis and Applications, Vol. 18, Issue 4, 2015, pp. 887-899.
Al Maadeed S., Hassaine A. Automatic prediction of age, gender, and nationality in offline handwriting. EURASIP Journal on Image
and Video Processing, Vol. 2014, No. 10, pp. 1-10.
Antonacopoulos A., Downton A. Special issue on the analysis of historical documents.Int. Journal on Document Analysis and Recognition, Vol. 9, No. 2-4, 2007, pp. 75-77.
Plamondon R., Lorette G. Automatic Signature Verification and Writer Identification – State art.Pattern Recognition, Vol. 22(2), 1989, pp. 107-131.
Awaida S.M., Mahmoud S.A. State of the art in off-line writer identification of handwritten text and survey of writer identification of Arabic text. Educational Research and Reviews, Vol. 7(20), 2012, pp. 445-463.
Schomaker L. Advances in Writer Identification and Verification. In Proc. of 9th Int’l Conf. on Document Analysis and Recognition, Vol.
, pp. 1268-1273.
Chaudhry R., Pant S.K. Identification of authorship using lateral palm print – a new concept. Forensic Science International, vol.
, 2004, 49-57.
Fornes A., Llados J., Sanchez G., Bunke H. Writer Identification in Old Handwritten Music Scores. In Proc. of 8th IAPR Workshop on
Document Analysis Systems, 2008, pp. 347-353.
Schlapbach A., Marcus L., Bunke H. A writer identification system for on-line whiteboard data. Pattern Recognition Journal, 41, 2008,
pp. 23821-23897.
Maarse F.J., Schomaker L.R.B., Teulings H.L. Automatic Identification of Writers in Human Computer Interaction: Psychonomic Aspects. Springer, Heidelberg, 1986.
Gupta S. Automatic Person Identification and Verification using Online Handwriting. Master Thesis. International Institute of Information Technology Hyderabad, India, 2008.
LeCun Y., Bottou L., Bengio Y., Haffner P. Gradient-based learning applied to document recognition. In Proc. of IEEE, Vol. 86 (11), 1998, pp. 2278–2324.
Marti U., Bunke N. A full English sentence database for off-line handwriting recognition.In Proc. of the 5th Int. Conf. on Document
Analysis and Recognition, 1999, pp. 705-708.
Grosicki E., Carré M., Brodin J-M., Geoffrois E. Results of the RIMES Evaluation Campaign for Handwritten Mail Processing. In Proc. of ICDAR 2009, pp.941-945.
Hull J.J. A database for handwritten text recognition research. IEEE Trans. Pattern Anal. Mach. Intell.,16(5), 1994, pp.550-554.
Kleber F., Fiel S., Diem M., Sablatnig R. CVL-database: An off-line database for writer retrieval, writer identification and word spotting.
In Proc. of the 12th Int. Conf. on Document Analysis and Recognition, 2013, pp. 560-564.
Pechwitz M., Maddouri S.S., Maergner V., Ellouze N., Amiri H. IFN/ENIT-database of handwritten Arabic words. In Proc. of CIFED, 2, 2002, pp. 127-136.
Amara N.B., Mazhoud O., Bouzrara N., Ellouze N. ARABASE: a relational database for Arabic OCR systems. Int. Arab J. Inf. Technol. 2(4), 2005, pp. 259-266.
Alamri H., Sadri J., Suen C.Y., Nobile N. A novel comprehensive database for Arabic off-line handwriting recognition. In Proc. of the
th Int. Conf. on Frontiers in Handwriting Recognition, 2008, pp. 664-669.
Kharma N., Ahmed M., Ward R. A new comprehensive database of handwritten Arabic words, numbers, and signatures used for OCR
testing. In IEEE Canadian Conf. on Electrical and Computer Engineering, 2, 1999, pp. 766-768.
Mahmoud S.A., Ahmad I., Al-Khatib W.G., Alshayeb M., Parvez M.T., Märgner V., Fink G.A. KHATT: An open Arabic offline handwritten text database. Pattern Recognition, 47(3), 2014, pp. 1096-1112.
Al Maadeed S., Ayouby W., Hassaine A., Aljaam J.M. QUWI: An Arabic and English handwriting dataset for offline writer identification. In Proc. of Int. Conf. on Frontiers in Handwriting Recognition, 2012, pp. 746-751.
Kim D., Hwang Y., Park S., Kim E., Paek S., Bang S. Handwritten Korean character image database Pe92.In Proc. of the 2nd Int. Conf.
on Document Analysis and Recognition, 1993, pp. 470-473.
Zhang H., Guo J., Chen G., Li C. HCL2000 - a large-scale handwritten Chinese character database for handwritten character recognition. In Proc. of the 10th Int. Conf. on Document Analysis and Recognition, 2009, pp. 286-290.
Liu C.L., Yin F., Wang D.H., Wang Q.F.CASIA online and offline Chinese handwriting databases. In Proc. of International Conference
on Document Analysis and Recognition, 2011, pp. 37-41.
Bidgoli A.M., Sarhadi M. IAUT/PHCN: Islamic Azad university of Tehran/Persian handwritten city names, a very large database of
handwritten Persian words. In Proc. of the International Conference on Frontiers in Handwriting Recognition, 2008, pp. 192-197.
Sagheer M.W., He C., Nobile N., Suen C.Y. A new large Urdu database for off-line handwriting recognition. Image Analysis and Processing Lecture Notes in Computer Science, 2009, pp. 538-546.
Jifroodian H.P., Nicola N., He C.L., Suen C.Y. A new large-scale multi-purpose handwritten Farsi database. Image Analysis and Recognition Lecture Notes in Computer Science, Vol. 5627, 2009, pp. 278-286.
Safabaksh R., Ghanbarian A.R., Ghiasi G. HaFT: A handwritten Farsi text database. In Proc. of the 8th Iranian Conference on Machine Vision and Image Processing, 2013, pp. 89-94.
Ma L., Liu H., Wu J. MRG-OHTC database for online handwritten Tibetan character recognition. In Proc. of the Int. Conf. on Document
Analysis and Recognition, 2011, pp. 207-211.
Kavallieratou E., Liolios N., Koutsogeorgos E., Fakotakis N., Kokkinakis G. The GRUHD database of Greek unconstrained handwriting. In Proc. of the 6th Int. Conf. on Document Analysis and Recognition, 2001, pp. 561-565.
Saady Y.E., Rachidi A., Yassa M. AMHCD: A database for Amazigh handwritten character recognition research. Int. Journal of Computer Applications, Vol. 27(4), 2011, pp. 44-48.
Hussain R., Raza A., Siddiqi I., Khurshid K., Djeddi C. A comprehensive survey of handwritten document benchmarks: Structure, Usage and Evaluation. EURASIP Journal on Image and Video Processing, Vol. 46, 2015, pp. 1-24.
Otsu N. A threshold selection method from gray-level histograms. IEEE Trans. Systems Man Cybernet., Vol. 9 (1), 1979, pp. 62-66.
Kittler J., Illingworth J. On threshold selection using clustering criteria. IEEE Trans. Systems Man Cybernet., Vol. 15, 1985, pp. 652-655.
Brink A.D. Thresholding of digital images using two-dimensional entropies. Pattern Recognition, Vol. 25 (8), 1992, pp. 803-808.
Yan H. Unified formulation of a class of image thresholding techniques. Pattern Recognition, Vol. 29 (12), 1996, pp. 2025-2032.
Sahoo P.K., Soltani S., Wong A.K.C. A survey of thresholding technique. Computer Vision, Graphics Image Processing, Vol. 41 (2), 1988, pp. 233-260.
Ntirogiannis K., Gatos B., Pratikakis I. A performance evaluation methodology for historical document image binarization. IEEE Transactions on Image Processing, Vol. 22, 2013, pp. 595-609.
Yang Y., Yan H. An adaptive logical method for binarization of degraded document images. Pattern Recognition Letter, Vol. 33, 2012, pp. 787-807.
Gatos B., Pratikakis I., Perantonis S. Adaptive degraded document image binarization. Pattern Recognition, Vol. 39, 2006, pp. 317-327.
Sauvola J., Pietikainen M. Adaptive document image binarization. Pattern Recognition, Vol. 33, 2000, pp. 225-236.
Bataineh B., Abdullah S., Omar K. An adaptive local binarization method for document images based on a novel thresholding method
and dynamic windows. Pattern Recognition Letter, Vol. 32, 2011, pp. 1805-1813.
Mandel I.D. Cluster analysis. Finance and Statistics, Moscow, 1988.
Kaufman L., Rousseeuw P.J. Finding Groups in Data: An Introduction to Cluster Analysis. John Wiley & Sons, New Jersey, 2005.
Li Y., Zheng Y., Doermann D., Jaeger S. Script-independent text line segmentation in freestyle handwritten documents. Pattern Analysis and Machine Intelligence, IEEE Transactions on, Vol.30 (8), 2008, pp. 1313-1329.
Zahour A., Taconet B., Mercy P., Ramdane S. Arabic hand-written text-line extraction. In Proc. of ICDAR’01, 2001, pp. 281-285.
Likforman-Sulem L., Zahour A., Taconet B. Text line segmentation of historical documents: a survey.Int. Journal of Document Analysis and Recognition, 9, 2-4, 2007, pp. 123-138.
Li Y., Zheng Y., Doermann D., Jaeger S. A new algorithm for detecting text line in handwritten documents. Int. Workshop on Frontiers in Handwriting Recognition, 2006, pp. 35-40.
Shi Z., Govindaraju V. Line Separation for Complex Document Images Using Fuzzy Runlength. In Proc. of the Int. Workshop on Document Image Analysis for Libraries, 2004, pp. 306-312.
Louloudis G., Gatos B., Pratikakis I., Halatsis K. A Block-Based Hough Transform Mapping for Text Line Detection in Handwritten Documents. In Proc. of the Tenth Int. Workshop on Frontiers in Handwriting Recognition, 2006.
Kim G., Govindaraju V., Srihari S.N. An architecture for handwritten text recognition systems. Int. Journal on Document Analysis and Recognition, 1999, 2, 1, pp. 37-44.
Likforman-Sulem L., Faure C. Extracting text lines in handwritten documents by perceptual grouping. Advances in handwriting and
drawing: a multidisciplinary approach, 1994, pp. 117-135.
Nakajima Y., Mori S., Takegami S., Sato S. Global methods for stroke segmentation. Int. Journal on Document Analysis and Recognition, 1999, 2, 1, pp. 19-23.
MestetskiyL.M. Continuous morphology of binary images. Figures. Skeletons. Circulars. FIZMAT-LIT, Moscow, 2009.
Seni G., Cohen E. External word segmentation of off-line handwritten text lines. Pattern Recognition, 27(1), 1994, pp. 41-52.
Marti U.V., Bunke H. Text line segmentation and word recognition in a system for general writer independent handwriting recognition.In Proc. of the 6th Int. Conf. on Document Analysis and Pattern Recognition, 2001, pp. 159-163.
Kapoor R., Bagai D., Kamal T.S. A new algorithm for skew detection and correction. Pattern Recognition Letters, Vol. 25, 2004, pp. 1215-1229.
Kennard D.J., Barrett W.A. Separating Lines of Text in Free-Form Handwritten Historical Documents. In Proc. of Int. Conf. on Document Image Analysis for Libraries, 2006, pp. 12-23.
Schlapbach A., Bunke H. Writer Identification Using an HMMBased Handwriting Recognition System: To Normalize the Input or Not? In Proc. of the 9th Int. Workshop on Frontiers in Handwriting Recognition, 2004, pp. 38-142.
Zhang T., Suen C. A fast parallel algorithm for thinning digital patterns.Communications of the ACM, Vol.27 (3), 1984, pp. 236-239.
Wu R.-Y., Tsai W.-H. A new one-pass parallel thinning algorithm for binary images. Pattern Recognition Letters, Vol.13 (10), 1992, pp. 715-723.
Siddiqi I., Vincent N. Stroke width independent feature for writer identification and handwriting classification. In Proc. of the Eleventh
Int. Conf. on Frontiers in Handwriting Recognition, 2008, pp. 76-81.
Hamdulla A., Naby G., Ubul K., Moydin K. Research on Uyghur Handwriting Identification Technology Based on Stroke Statistical Features.Int. Journal of Signal Processing, Image Processing and Pattern Recognition, Vol.7 (1), 2014, pp. 415-424.
Tou J., GonzalezR. Pattern Recognition Principles. Addison-Wesley, Reading, MA, 1974.
Szeliski R. Computer Vision: Algorithms and Applications. Springer, New York, 2011.
Nixon M., Aguado A. Feature Extraction and Image Processing. Elsevier, New York, 2008.
Soyfer V. et al. Computer image processing methods. Fizmatlit, Moscow, 2003. – 784 с.
Duda R., Hart P., Stork D. Pattern Classification. John Wiley & Sons, New York, 2001.
Pratt W.K. Digital image processing. John Wiley & Sons, New York, 2001.
Marti U.V., Messerli R., Bunke H. Writer identification using text line based features.In Proc. of 6th Int. Conf.on Document Analysis and Recognition, 2001, pp. 101-105.
Hertel C., Bunke H. A Set of Novel Features for Writer Identification. In Proc. of Fourth Int. Conf.on Audio and VideoBased Biometric Person Authentication, 2003, pp. 679-687.
Leedham G., Chachra S. Writer identification using innovative binarised features of handwritten numerals. In Proc. of Seventh Int.
Conf.on Document Analysis and Recognition, 2003.
SuyasovD.I. Isolation of structural features of symbol images based on cellular automata with labels. Information and control systems, 2010, Vol. 4, pp. 39-45.
Zhang B., Srihari S., Lee S. Individuality of Handwritten Characters. In Proc. of Seventh Int. Conf. on Document Analysis and Recognition, 2003, pp. 1086-1090.
Tomai C., Zhang B., Srihari S. Discriminatory power of handwritten words for writer recognition. In Proc. of Int. Conf. on Pattern Recognition, Vol. 2, 2004, pp. 638-641.
Siddiqi I., Vincent N. A Set of Chain Code Based Features for Writer Recognition. In Proc. of Tenth Int. Conf.on Document Analysis and Recognition, 2009, pp. 981-985.
ShlezingerM., GlavachV. Ten lectures on statistical and structural recognition. Naukovadumka, Kiev, 2004.
Bulacu M., Schomaker L., Vuurpijl L. Writer identification using edge-based directional features. In Proc. of Seventh Int. Conf.on Document Analysis and Recognition, 2003, pp. 937-941.
Yan Y., Chen Q., Deng W., Yuan F. Chinese Handwriting Identification Based on Stable Spectral Feature of Texture Images. Int.Journal of Intelligent Engineering and Systems, Vol.2, No.1, 2009, pp. 17-22.
Al-Dmour A., Zitar R.A. Arabic writer identification based on hybrid spectral-statistical measures. Journal of Experimental & Theoretical Artificial Intelligence, 2007, 19(4), p. 307–332.
Srihari S., Cha S.-H., Arora H., Lee S. Individuality of Handwriting. Journal of Forensic Sciences, 2002, vol. 47, no. 4, pp. 1-17.
Srihari S., Cha S.-H., Lee S. Establishing Handwriting Individuality Using Pattern Recognition Techniques. In Proc. of the Sixth Int.
Conf. on Document Analysis and Recognition, 2001, pp.1195-1204.
Christlein V., Bernecker D., Maier A., Angelopoulou E. Offline Writer Identification Using Convolutional Neural Network Activation Features. In Proc. of the German Conf. on Pattern Recognition, pp. 540-552, 2015.
Zhu Y., Tan T., Wang Y. Biometric Personal Identification Based on Handwriting. In: Proc. 15th Int. Conf. on Pattern Recognition, 2000,
Vol. 2, pp. 801–804.
Schomaker L., Bulacu M., Franke K. Automatic Writer Identification Using Fragmented Connected-Component Contours. In Proc. of 9th Int. Workshop on Frontiers in Handwriting Recognition, 2004, pp. 185–190.
Schlapbach A. Writer Identification and Verification. Clifton VA, IOS Press, 2007.
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