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Publisher: Springer-Verlag (Total: 2350 journals)

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Showing 1 - 200 of 2350 Journals sorted alphabetically
3D Printing in Medicine     Open Access   (Followers: 1)
3D Research     Hybrid Journal   (Followers: 21, SJR: 0.222, CiteScore: 1)
4OR: A Quarterly J. of Operations Research     Hybrid Journal   (Followers: 10, SJR: 0.825, CiteScore: 1)
AAPS J.     Hybrid Journal   (Followers: 22, SJR: 1.118, CiteScore: 4)
AAPS PharmSciTech     Hybrid Journal   (Followers: 7, SJR: 0.752, CiteScore: 3)
Abdominal Imaging     Hybrid Journal   (Followers: 15, SJR: 0.866, CiteScore: 2)
Abhandlungen aus dem Mathematischen Seminar der Universitat Hamburg     Hybrid Journal   (Followers: 4, SJR: 0.439, CiteScore: 0)
Academic Psychiatry     Full-text available via subscription   (Followers: 25, SJR: 0.53, CiteScore: 1)
Academic Questions     Hybrid Journal   (Followers: 8, SJR: 0.106, CiteScore: 0)
Accreditation and Quality Assurance: J. for Quality, Comparability and Reliability in Chemical Measurement     Hybrid Journal   (Followers: 27, SJR: 0.316, CiteScore: 1)
Acoustical Physics     Hybrid Journal   (Followers: 11, SJR: 0.359, CiteScore: 1)
Acoustics Australia     Hybrid Journal   (SJR: 0.232, CiteScore: 1)
Acta Analytica     Hybrid Journal   (Followers: 7, SJR: 0.367, CiteScore: 0)
Acta Applicandae Mathematicae     Hybrid Journal   (Followers: 1, SJR: 0.675, CiteScore: 1)
Acta Biotheoretica     Hybrid Journal   (Followers: 4, SJR: 0.284, CiteScore: 1)
Acta Diabetologica     Hybrid Journal   (Followers: 18, SJR: 1.587, CiteScore: 3)
Acta Endoscopica     Hybrid Journal   (Followers: 1)
acta ethologica     Hybrid Journal   (Followers: 4, SJR: 0.769, CiteScore: 1)
Acta Geochimica     Hybrid Journal   (Followers: 7, SJR: 0.24, CiteScore: 1)
Acta Geodaetica et Geophysica     Hybrid Journal   (Followers: 2, SJR: 0.305, CiteScore: 1)
Acta Geophysica     Hybrid Journal   (Followers: 10, SJR: 0.312, CiteScore: 1)
Acta Geotechnica     Hybrid Journal   (Followers: 7, SJR: 1.588, CiteScore: 3)
Acta Informatica     Hybrid Journal   (Followers: 5, SJR: 0.517, CiteScore: 1)
Acta Mathematica     Hybrid Journal   (Followers: 12, SJR: 7.066, CiteScore: 3)
Acta Mathematica Hungarica     Hybrid Journal   (Followers: 2, SJR: 0.452, CiteScore: 1)
Acta Mathematica Sinica, English Series     Hybrid Journal   (Followers: 6, SJR: 0.379, CiteScore: 1)
Acta Mathematica Vietnamica     Hybrid Journal   (SJR: 0.27, CiteScore: 0)
Acta Mathematicae Applicatae Sinica, English Series     Hybrid Journal   (SJR: 0.208, CiteScore: 0)
Acta Mechanica     Hybrid Journal   (Followers: 21, SJR: 1.04, CiteScore: 2)
Acta Mechanica Sinica     Hybrid Journal   (Followers: 5, SJR: 0.607, CiteScore: 2)
Acta Metallurgica Sinica (English Letters)     Hybrid Journal   (Followers: 7, SJR: 0.576, CiteScore: 2)
Acta Meteorologica Sinica     Hybrid Journal   (Followers: 3, SJR: 0.638, CiteScore: 1)
Acta Neurochirurgica     Hybrid Journal   (Followers: 6, SJR: 0.822, CiteScore: 2)
Acta Neurologica Belgica     Hybrid Journal   (Followers: 1, SJR: 0.376, CiteScore: 1)
Acta Neuropathologica     Hybrid Journal   (Followers: 5, SJR: 7.589, CiteScore: 12)
Acta Oceanologica Sinica     Hybrid Journal   (Followers: 3, SJR: 0.334, CiteScore: 1)
Acta Physiologiae Plantarum     Hybrid Journal   (Followers: 2, SJR: 0.574, CiteScore: 2)
Acta Politica     Hybrid Journal   (Followers: 15, SJR: 0.605, CiteScore: 1)
Activitas Nervosa Superior     Hybrid Journal   (SJR: 0.147, CiteScore: 0)
adhäsion KLEBEN & DICHTEN     Hybrid Journal   (Followers: 8, SJR: 0.103, CiteScore: 0)
ADHD Attention Deficit and Hyperactivity Disorders     Hybrid Journal   (Followers: 23, SJR: 0.72, CiteScore: 2)
Adhesion Adhesives & Sealants     Hybrid Journal   (Followers: 9)
Administration and Policy in Mental Health and Mental Health Services Research     Partially Free   (Followers: 16, SJR: 1.005, CiteScore: 2)
Adsorption     Hybrid Journal   (Followers: 4, SJR: 0.703, CiteScore: 2)
Advances in Applied Clifford Algebras     Hybrid Journal   (Followers: 4, SJR: 0.698, CiteScore: 1)
Advances in Atmospheric Sciences     Hybrid Journal   (Followers: 37, SJR: 0.956, CiteScore: 2)
Advances in Computational Mathematics     Hybrid Journal   (Followers: 19, SJR: 0.812, CiteScore: 1)
Advances in Contraception     Hybrid Journal   (Followers: 3)
Advances in Data Analysis and Classification     Hybrid Journal   (Followers: 54, SJR: 1.09, CiteScore: 1)
Advances in Gerontology     Partially Free   (Followers: 8, SJR: 0.144, CiteScore: 0)
Advances in Health Sciences Education     Hybrid Journal   (Followers: 28, SJR: 1.64, CiteScore: 2)
Advances in Manufacturing     Hybrid Journal   (Followers: 3, SJR: 0.475, CiteScore: 2)
Advances in Polymer Science     Hybrid Journal   (Followers: 44, SJR: 1.04, CiteScore: 3)
Advances in Therapy     Hybrid Journal   (Followers: 5, SJR: 1.075, CiteScore: 3)
Aegean Review of the Law of the Sea and Maritime Law     Hybrid Journal   (Followers: 6)
Aequationes Mathematicae     Hybrid Journal   (Followers: 2, SJR: 0.517, CiteScore: 1)
Aerobiologia     Hybrid Journal   (Followers: 3, SJR: 0.673, CiteScore: 2)
Aesthetic Plastic Surgery     Hybrid Journal   (Followers: 9, SJR: 0.825, CiteScore: 1)
African Archaeological Review     Hybrid Journal   (Followers: 16, SJR: 0.862, CiteScore: 1)
Afrika Matematika     Hybrid Journal   (Followers: 1, SJR: 0.235, CiteScore: 0)
AGE     Hybrid Journal   (Followers: 7)
Ageing Intl.     Hybrid Journal   (Followers: 7, SJR: 0.39, CiteScore: 1)
Aggiornamenti CIO     Hybrid Journal   (Followers: 1)
Aging Clinical and Experimental Research     Hybrid Journal   (Followers: 3, SJR: 0.67, CiteScore: 2)
Agricultural Research     Hybrid Journal   (Followers: 6, SJR: 0.276, CiteScore: 1)
Agriculture and Human Values     Hybrid Journal   (Followers: 14, SJR: 1.173, CiteScore: 3)
Agroforestry Systems     Hybrid Journal   (Followers: 20, SJR: 0.663, CiteScore: 1)
Agronomy for Sustainable Development     Hybrid Journal   (Followers: 12, SJR: 1.864, CiteScore: 6)
AI & Society     Hybrid Journal   (Followers: 8, SJR: 0.227, CiteScore: 1)
AIDS and Behavior     Hybrid Journal   (Followers: 14, SJR: 1.792, CiteScore: 3)
Air Quality, Atmosphere & Health     Hybrid Journal   (Followers: 4, SJR: 0.862, CiteScore: 3)
Akupunktur & Aurikulomedizin     Full-text available via subscription   (Followers: 1)
Algebra and Logic     Hybrid Journal   (Followers: 6, SJR: 0.531, CiteScore: 0)
Algebra Universalis     Hybrid Journal   (Followers: 2, SJR: 0.583, CiteScore: 1)
Algebras and Representation Theory     Hybrid Journal   (Followers: 1, SJR: 1.095, CiteScore: 1)
Algorithmica     Hybrid Journal   (Followers: 9, SJR: 0.56, CiteScore: 1)
Allergo J.     Full-text available via subscription   (Followers: 1, SJR: 0.234, CiteScore: 0)
Allergo J. Intl.     Hybrid Journal   (Followers: 2)
Alpine Botany     Hybrid Journal   (Followers: 5, SJR: 1.11, CiteScore: 3)
ALTEX : Alternatives to Animal Experimentation     Open Access   (Followers: 3)
AMBIO     Hybrid Journal   (Followers: 10, SJR: 1.569, CiteScore: 4)
American J. of Cardiovascular Drugs     Hybrid Journal   (Followers: 16, SJR: 0.951, CiteScore: 3)
American J. of Community Psychology     Hybrid Journal   (Followers: 28, SJR: 1.329, CiteScore: 2)
American J. of Criminal Justice     Hybrid Journal   (Followers: 8, SJR: 0.772, CiteScore: 1)
American J. of Cultural Sociology     Hybrid Journal   (Followers: 16, SJR: 0.46, CiteScore: 1)
American J. of Dance Therapy     Hybrid Journal   (Followers: 4, SJR: 0.181, CiteScore: 0)
American J. of Potato Research     Hybrid Journal   (Followers: 2, SJR: 0.611, CiteScore: 1)
American J. of Psychoanalysis     Hybrid Journal   (Followers: 21, SJR: 0.314, CiteScore: 0)
American Sociologist     Hybrid Journal   (Followers: 12, SJR: 0.35, CiteScore: 0)
Amino Acids     Hybrid Journal   (Followers: 8, SJR: 1.135, CiteScore: 3)
AMS Review     Partially Free   (Followers: 4)
Analog Integrated Circuits and Signal Processing     Hybrid Journal   (Followers: 7, SJR: 0.211, CiteScore: 1)
Analysis and Mathematical Physics     Hybrid Journal   (Followers: 5, SJR: 0.536, CiteScore: 1)
Analysis in Theory and Applications     Hybrid Journal   (Followers: 1)
Analysis of Verbal Behavior     Hybrid Journal   (Followers: 5)
Analytical and Bioanalytical Chemistry     Hybrid Journal   (Followers: 32, SJR: 0.978, CiteScore: 3)
Anatomical Science Intl.     Hybrid Journal   (Followers: 2, SJR: 0.367, CiteScore: 1)
Angewandte Schmerztherapie und Palliativmedizin     Hybrid Journal  
Angiogenesis     Hybrid Journal   (Followers: 3, SJR: 2.177, CiteScore: 5)
Animal Cognition     Hybrid Journal   (Followers: 19, SJR: 1.389, CiteScore: 3)
Annales françaises de médecine d'urgence     Hybrid Journal   (Followers: 1, SJR: 0.192, CiteScore: 0)
Annales Henri Poincaré     Hybrid Journal   (Followers: 3, SJR: 1.097, CiteScore: 2)
Annales mathématiques du Québec     Hybrid Journal   (Followers: 4, SJR: 0.438, CiteScore: 0)
Annali dell'Universita di Ferrara     Hybrid Journal   (SJR: 0.429, CiteScore: 0)
Annali di Matematica Pura ed Applicata     Hybrid Journal   (Followers: 1, SJR: 1.197, CiteScore: 1)
Annals of Biomedical Engineering     Hybrid Journal   (Followers: 18, SJR: 1.042, CiteScore: 3)
Annals of Combinatorics     Hybrid Journal   (Followers: 4, SJR: 0.932, CiteScore: 1)
Annals of Data Science     Hybrid Journal   (Followers: 12)
Annals of Dyslexia     Hybrid Journal   (Followers: 10, SJR: 0.85, CiteScore: 2)
Annals of Finance     Hybrid Journal   (Followers: 30, SJR: 0.579, CiteScore: 1)
Annals of Forest Science     Hybrid Journal   (Followers: 7, SJR: 0.986, CiteScore: 2)
Annals of Global Analysis and Geometry     Hybrid Journal   (Followers: 1, SJR: 1.228, CiteScore: 1)
Annals of Hematology     Hybrid Journal   (Followers: 15, SJR: 1.043, CiteScore: 2)
Annals of Mathematics and Artificial Intelligence     Hybrid Journal   (Followers: 12, SJR: 0.413, CiteScore: 1)
Annals of Microbiology     Hybrid Journal   (Followers: 11, SJR: 0.479, CiteScore: 2)
Annals of Nuclear Medicine     Hybrid Journal   (Followers: 4, SJR: 0.687, CiteScore: 2)
Annals of Operations Research     Hybrid Journal   (Followers: 10, SJR: 0.943, CiteScore: 2)
Annals of Ophthalmology     Hybrid Journal   (Followers: 11)
Annals of Regional Science     Hybrid Journal   (Followers: 7, SJR: 0.614, CiteScore: 1)
Annals of Software Engineering     Hybrid Journal   (Followers: 13)
Annals of Solid and Structural Mechanics     Hybrid Journal   (Followers: 9, SJR: 0.239, CiteScore: 1)
Annals of Surgical Oncology     Hybrid Journal   (Followers: 13, SJR: 1.986, CiteScore: 4)
Annals of Telecommunications     Hybrid Journal   (Followers: 9, SJR: 0.223, CiteScore: 1)
Annals of the Institute of Statistical Mathematics     Hybrid Journal   (Followers: 1, SJR: 1.495, CiteScore: 1)
Antonie van Leeuwenhoek     Hybrid Journal   (Followers: 5, SJR: 0.834, CiteScore: 2)
Apidologie     Hybrid Journal   (Followers: 4, SJR: 1.22, CiteScore: 3)
APOPTOSIS     Hybrid Journal   (Followers: 8, SJR: 1.424, CiteScore: 4)
Applicable Algebra in Engineering, Communication and Computing     Hybrid Journal   (Followers: 2, SJR: 0.294, CiteScore: 1)
Applications of Mathematics     Hybrid Journal   (Followers: 2, SJR: 0.602, CiteScore: 1)
Applied Biochemistry and Biotechnology     Hybrid Journal   (Followers: 43, SJR: 0.571, CiteScore: 2)
Applied Biochemistry and Microbiology     Hybrid Journal   (Followers: 17, SJR: 0.21, CiteScore: 1)
Applied Cancer Research     Open Access  
Applied Categorical Structures     Hybrid Journal   (Followers: 2, SJR: 0.49, CiteScore: 0)
Applied Composite Materials     Hybrid Journal   (Followers: 49, SJR: 0.58, CiteScore: 2)
Applied Entomology and Zoology     Partially Free   (Followers: 4, SJR: 0.422, CiteScore: 1)
Applied Geomatics     Hybrid Journal   (Followers: 3, SJR: 0.733, CiteScore: 3)
Applied Geophysics     Hybrid Journal   (Followers: 8, SJR: 0.488, CiteScore: 1)
Applied Intelligence     Hybrid Journal   (Followers: 12, SJR: 0.6, CiteScore: 2)
Applied Magnetic Resonance     Hybrid Journal   (Followers: 4, SJR: 0.319, CiteScore: 1)
Applied Mathematics & Optimization     Hybrid Journal   (Followers: 6, SJR: 0.886, CiteScore: 1)
Applied Mathematics - A J. of Chinese Universities     Hybrid Journal   (SJR: 0.17, CiteScore: 0)
Applied Mathematics and Mechanics     Hybrid Journal   (Followers: 5, SJR: 0.461, CiteScore: 1)
Applied Microbiology and Biotechnology     Hybrid Journal   (Followers: 64, SJR: 1.182, CiteScore: 4)
Applied Physics A     Hybrid Journal   (Followers: 9, SJR: 0.481, CiteScore: 2)
Applied Physics B: Lasers and Optics     Hybrid Journal   (Followers: 24, SJR: 0.74, CiteScore: 2)
Applied Psychophysiology and Biofeedback     Hybrid Journal   (Followers: 8, SJR: 0.519, CiteScore: 2)
Applied Research in Quality of Life     Hybrid Journal   (Followers: 12, SJR: 0.316, CiteScore: 1)
Applied Solar Energy     Hybrid Journal   (Followers: 18, SJR: 0.225, CiteScore: 0)
Applied Spatial Analysis and Policy     Hybrid Journal   (Followers: 5, SJR: 0.542, CiteScore: 1)
Aquaculture Intl.     Hybrid Journal   (Followers: 24, SJR: 0.591, CiteScore: 2)
Aquarium Sciences and Conservation     Hybrid Journal   (Followers: 2)
Aquatic Ecology     Hybrid Journal   (Followers: 34, SJR: 0.656, CiteScore: 2)
Aquatic Geochemistry     Hybrid Journal   (Followers: 4, SJR: 0.591, CiteScore: 1)
Aquatic Sciences     Hybrid Journal   (Followers: 13, SJR: 1.109, CiteScore: 3)
Arabian J. for Science and Engineering     Hybrid Journal   (Followers: 5, SJR: 0.303, CiteScore: 1)
Arabian J. of Geosciences     Hybrid Journal   (Followers: 2, SJR: 0.319, CiteScore: 1)
Archaeological and Anthropological Sciences     Hybrid Journal   (Followers: 20, SJR: 1.052, CiteScore: 2)
Archaeologies     Hybrid Journal   (Followers: 12, SJR: 0.224, CiteScore: 0)
Archiv der Mathematik     Hybrid Journal   (Followers: 1, SJR: 0.725, CiteScore: 1)
Archival Science     Hybrid Journal   (Followers: 60, SJR: 0.745, CiteScore: 2)
Archive for History of Exact Sciences     Hybrid Journal   (Followers: 7, SJR: 0.186, CiteScore: 1)
Archive for Mathematical Logic     Hybrid Journal   (Followers: 3, SJR: 0.909, CiteScore: 1)
Archive for Rational Mechanics and Analysis     Hybrid Journal   (SJR: 3.93, CiteScore: 3)
Archive of Applied Mechanics     Hybrid Journal   (Followers: 5, SJR: 0.79, CiteScore: 2)
Archives and Museum Informatics     Hybrid Journal   (Followers: 143, SJR: 0.101, CiteScore: 0)
Archives of Computational Methods in Engineering     Hybrid Journal   (Followers: 5, SJR: 1.41, CiteScore: 5)
Archives of Dermatological Research     Hybrid Journal   (Followers: 7, SJR: 1.006, CiteScore: 2)
Archives of Environmental Contamination and Toxicology     Hybrid Journal   (Followers: 14, SJR: 0.773, CiteScore: 2)
Archives of Gynecology and Obstetrics     Hybrid Journal   (Followers: 16, SJR: 0.956, CiteScore: 2)
Archives of Microbiology     Hybrid Journal   (Followers: 8, SJR: 0.644, CiteScore: 2)
Archives of Orthopaedic and Trauma Surgery     Hybrid Journal   (Followers: 8, SJR: 1.146, CiteScore: 2)
Archives of Osteoporosis     Hybrid Journal   (Followers: 2, SJR: 0.71, CiteScore: 2)
Archives of Sexual Behavior     Hybrid Journal   (Followers: 10, SJR: 1.493, CiteScore: 3)
Archives of Toxicology     Hybrid Journal   (Followers: 17, SJR: 1.541, CiteScore: 5)
Archives of Virology     Hybrid Journal   (Followers: 5, SJR: 0.973, CiteScore: 2)
Archives of Women's Mental Health     Hybrid Journal   (Followers: 14, SJR: 1.274, CiteScore: 3)
Archivio di Ortopedia e Reumatologia     Hybrid Journal  
Archivum Immunologiae et Therapiae Experimentalis     Hybrid Journal   (Followers: 2, SJR: 0.946, CiteScore: 3)
ArgoSpine News & J.     Hybrid Journal  
Argumentation     Hybrid Journal   (Followers: 6, SJR: 0.349, CiteScore: 1)
Arid Ecosystems     Hybrid Journal   (Followers: 2, SJR: 0.2, CiteScore: 0)
Arkiv för Matematik     Hybrid Journal   (Followers: 1, SJR: 0.766, CiteScore: 1)
Arnold Mathematical J.     Hybrid Journal   (Followers: 1, SJR: 0.355, CiteScore: 0)
Arthropod-Plant Interactions     Hybrid Journal   (Followers: 2, SJR: 0.839, CiteScore: 2)
Arthroskopie     Hybrid Journal   (Followers: 1, SJR: 0.131, CiteScore: 0)
Artificial Intelligence and Law     Hybrid Journal   (Followers: 11, SJR: 0.937, CiteScore: 2)
Artificial Intelligence Review     Hybrid Journal   (Followers: 15, SJR: 0.833, CiteScore: 4)
Artificial Life and Robotics     Hybrid Journal   (Followers: 9, SJR: 0.226, CiteScore: 0)
Asia Europe J.     Hybrid Journal   (Followers: 5, SJR: 0.504, CiteScore: 1)
Asia Pacific Education Review     Hybrid Journal   (Followers: 12, SJR: 0.479, CiteScore: 1)
Asia Pacific J. of Management     Hybrid Journal   (Followers: 16, SJR: 1.185, CiteScore: 2)
Asia-Pacific Education Researcher     Hybrid Journal   (Followers: 12, SJR: 0.353, CiteScore: 1)
Asia-Pacific Financial Markets     Hybrid Journal   (Followers: 2, SJR: 0.187, CiteScore: 0)
Asia-Pacific J. of Atmospheric Sciences     Hybrid Journal   (Followers: 19, SJR: 0.855, CiteScore: 1)
Asian Business & Management     Hybrid Journal   (Followers: 9, SJR: 0.378, CiteScore: 1)
Asian J. of Business Ethics     Hybrid Journal   (Followers: 9)
Asian J. of Criminology     Hybrid Journal   (Followers: 6, SJR: 0.543, CiteScore: 1)
AStA Advances in Statistical Analysis     Hybrid Journal   (Followers: 3, SJR: 0.548, CiteScore: 1)
AStA Wirtschafts- und Sozialstatistisches Archiv     Hybrid Journal   (Followers: 5, SJR: 0.183, CiteScore: 0)
ästhetische dermatologie & kosmetologie     Full-text available via subscription  

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Journal Cover
International Journal on Document Analysis and Recognition (IJDAR)
Journal Prestige (SJR): 0.456
Citation Impact (citeScore): 2
Number of Followers: 2  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1433-2833 - ISSN (Online) 1433-2825
Published by Springer-Verlag Homepage  [2350 journals]
  • Recognition-based character segmentation for multi-level writing style
    • Authors: Papangkorn Inkeaw; Jakramate Bootkrajang; Phasit Charoenkwan; Sanparith Marukatat; Shinn-Ying Ho; Jeerayut Chaijaruwanich
      Pages: 21 - 39
      Abstract: Abstract Character segmentation is an important task in optical character recognition (OCR). The quality of any OCR system is highly dependent on character segmentation algorithm. Despite the availability of various character segmentation methods proposed to date, existing methods cannot satisfyingly segment characters belonging to some complex writing styles such as the Lanna Dhamma characters. In this paper, a new character segmentation method named graph partitioning-based character segmentation is proposed to address the problem. The proposed method can deal with multi-level writing style as well as touching and broken characters. It is considered as a generalization of existing approaches to multi-level writing style. The proposed method consists of three phases. In the first phase, a newly devised over-segmentation technique based on morphological skeleton is used to obtain redundant fragments of a word image. The fragments are then used to form a segmentation hypotheses graph. In the last phase, the hypotheses graph is partitioned into subgraphs each corresponding to a segmented character using the partitioning algorithm developed specifically for character segmentation purpose. Experimental results based on handwritten Lanna Dhamma characters datasets showed that the proposed method achieved high correct segmentation rate and outperformed existing methods for the Lanna Dhamma alphabet.
      PubDate: 2018-06-01
      DOI: 10.1007/s10032-018-0302-5
      Issue No: Vol. 21, No. 1-2 (2018)
       
  • Fusion of LLE and stochastic LEM for Persian handwritten digits
           recognition
    • Authors: Rassoul Hajizadeh; A. Aghagolzadeh; M. Ezoji
      Pages: 109 - 122
      Abstract: Abstract In this paper, a new local manifold learning (ML) method is proposed. Our proposed method, which is named FSLL, is based on the fusion of locally linear embedding (LLE) and a new Stochastic Laplacian Eigenmaps (SLEM). SLEM is the same as a common LEM technique, but the coefficients between each data point and its neighbors are calculated by a stochastic process. The coefficients of SLEM make a probability mass function scheme, and their entropy is set to a certain value. The entropy value is an estimation of the locality around each data point. Two criteria will be presented based on the mutual neighborhood conception to determine the entropy value. In LLE, each data point is linearly reconstructed based on its neighbors and then the embedded data manifold is extracted by preserving these linear reconstruction coefficients. LLE and SLEM extract and learn the embedded data manifold by two different kinds of local structure information. In FSLL, two local ML methods, SLEM and LLE, are fused by rewriting their cost functions without the need for any projection space. Fusion of these two techniques provides more structural information at high-dimensional space that can be applied on extracting the embedded low-dimensional data. Also, in this study, a feature vector will be presented by combining a HMAX feature vector and a PCA-based feature vector. Evaluations of the proposed method are done on Persian handwritten digit IFHCDB and IPHD databases in image and feature spaces. The results demonstrate the performance of FSLL and SLEM. The recognition rates are improved about 4% in most dimensionalities. Also, a method of out-of-sample test data extension is proposed corresponding to the proposed methods.
      PubDate: 2018-06-01
      DOI: 10.1007/s10032-018-0303-4
      Issue No: Vol. 21, No. 1-2 (2018)
       
  • Fully convolutional network with dilated convolutions for handwritten text
           line segmentation
    • Authors: Guillaume Renton; Yann Soullard; Clément Chatelain; Sébastien Adam; Christopher Kermorvant; Thierry Paquet
      Abstract: Abstract We present a learning-based method for handwritten text line segmentation in document images. Our approach relies on a variant of deep fully convolutional networks (FCNs) with dilated convolutions. Dilated convolutions allow to never reduce the input resolution and produce a pixel-level labeling. The FCN is trained to identify X-height labeling as text line representation, which has many advantages for text recognition. We show that our approach outperforms the most popular variants of FCN, based on deconvolution or unpooling layers, on a public dataset. We also provide results investigating various settings, and we conclude with a comparison of our model with recent approaches defined as part of the cBAD (https://scriptnet.iit.demokritos.gr/competitions/5/) international competition, leading us to a 91.3% F-measure.
      PubDate: 2018-05-30
      DOI: 10.1007/s10032-018-0304-3
       
  • Text box proposals for handwritten word spotting from documents
    • Authors: Suman Ghosh; Ernest Valveny
      Abstract: Abstract In this article, we propose a new approach to segmentation-free word spotting that is based on the combination of three different contributions. Firstly, inspired by the success of bounding box proposal algorithms in object recognition, we propose a scheme to generate a set of word-independent text box proposals. For that, we generate a set of atomic bounding boxes based on simple connected component analysis that are combined using a set of spatial constraints in order to generate the final set of text box proposals. Secondly, an attribute representation based on the Pyramidal Histogram of Characters (PHOC) is encoded in an integral image and used to efficiently evaluate text box proposals for retrieval. Thirdly, we also propose an indexing scheme for fast retrieval based on character n-grams. For the generation of the index a similar attribute space based on a Pyramidal Histogram of Character N-grams (PHON) is used. All attribute models are learned using linear SVMs over the Fisher Vector representation of the word images along with the PHOC or PHON labels of the corresponding words. We show the performance of the proposed approach in both tasks of query-by-string and query-by-example in standard single- and multi-writer data sets, reporting state-of-the-art results.
      PubDate: 2018-04-27
      DOI: 10.1007/s10032-018-0300-7
       
  • Fixed-sized representation learning from offline handwritten signatures of
           different sizes
    • Authors: Luiz G. Hafemann; Luiz S. Oliveira; Robert Sabourin
      Abstract: Abstract Methods for learning feature representations for offline handwritten signature verification have been successfully proposed in recent literature, using deep convolutional neural networks to learn representations from signature pixels. Such methods reported large performance improvements compared to handcrafted feature extractors. However, they also introduced an important constraint: the inputs to the neural networks must have a fixed size, while signatures vary significantly in size between different users. In this paper, we propose addressing this issue by learning a fixed-sized representation from variable-sized signatures by modifying the network architecture, using spatial pyramid pooling. We also investigate the impact of the resolution of the images used for training and the impact of adapting (fine-tuning) the representations to new operating conditions (different acquisition protocols, such as writing instruments and scan resolution). On the GPDS dataset, we achieve results comparable with the state of the art, while removing the constraint of having a maximum size for the signatures to be processed. We also show that using higher resolutions (300 or 600 dpi) can improve performance when skilled forgeries from a subset of users are available for feature learning, but lower resolutions (around 100dpi) can be used if only genuine signatures are used. Lastly, we show that fine-tuning can improve performance when the operating conditions change.
      PubDate: 2018-04-23
      DOI: 10.1007/s10032-018-0301-6
       
  • Binarization of degraded document images based on contrast enhancement
    • Authors: Di Lu; Xin Huang; LiXue Sui
      Abstract: Abstract Because of the different types of document degradation such as uneven illumination, image contrast variation, blur caused by humidity, and bleed-through, degraded document image binarization is still an enormous challenge. This paper presents a new binarization method for degraded document images. The proposed algorithm focuses on the differences of image grayscale contrast in different areas. Quadtree is used to divide areas adaptively. In addition, various contrast enhancements are selected to adjust local grayscale contrast in areas with different contrasts. Finally, the local threshold is regarded as the mean of foreground and background gray values, which are determined by the frequency of the gray values. The proposed algorithm was tested on the datasets from the Document Image Binarization Contest (DIBCO) (DIBCO 2009, H-DIBCO 2010, DIBCO 2011, and H-DIBCO 2012). Compared with five other classical algorithms, the images binarized using the proposed algorithm achieved the highest F-measure and peak signal-to-noise ratio and obtained the highest correct rate of recognition.
      PubDate: 2018-04-06
      DOI: 10.1007/s10032-018-0299-9
       
  • A novel Arabic OCR post-processing using rule-based and word context
           techniques
    • Authors: Iyad Abu Doush; Faisal Alkhateeb; Anwaar Hamdi Gharaibeh
      Abstract: Abstract Optical character recognition (OCR) is the process of recognizing characters automatically from scanned documents for editing, indexing, searching, and reducing the storage space. The resulted text from the OCR usually does not match the text in the original document. In order to minimize the number of incorrect words in the obtained text, OCR post-processing approaches can be used. Correcting OCR errors is more complicated when we are dealing with the Arabic language because of its complexity such as connected letters, different letters may have the same shape, and the same letter may have different forms. This paper provides a statistical Arabic language model and post-processing techniques based on hybridizing the error model approach with the context approach. The proposed model is language independent and non-constrained with the string length. To the best of our knowledge, this is the first end-to-end OCR post-processing model that is applied to the Arabic language. In order to train the proposed model, we build Arabic OCR context database which contains 9000 images of Arabic text. Also, the evaluation of the OCR post-processing system results is automated using our novel alignment technique which is called fast automatic hashing text alignment. Our experimental results show that the rule-based system improves the word error rate from 24.02% to become 20.26% by using a training data set of 1000 images. On the other hand, after this training, we apply the rule-based system on 500 images as a testing dataset and the word error rate is improved from 14.95% to become 14.53%. The proposed hybrid OCR post-processing system improves the results based on using 1000 training images from a word error rate of 24.02% to become 18.96%. After training the hybrid system, we used 500 images for testing and the results show that the word error rate enhanced from 14.95 to become 14.42. The obtained results show that the proposed hybrid system outperforms the rule-based system.
      PubDate: 2018-04-05
      DOI: 10.1007/s10032-018-0297-y
       
  • Making scanned Arabic documents machine accessible using an ensemble of
           SVM classifiers
    • Authors: Randa Elanwar; Wenda Qin; Margrit Betke
      Abstract: Abstract Raster-image PDF files originating from scanning or photographing paper documents are inaccessible to both text search engines and screen readers that people with visual impairments use. We here focus on the relatively less-researched problem of converting raster-image files with Arabic script into machine-accessible documents. Our method, called ECDP for “Ensemble-based classification of document patches,” segments the physical layout of the document, classifies image patches as containing text or graphics, assembles homogeneous document regions, and passes the text to an optical character recognition engine to convert into natural language. Classification is based on the majority voting of an ensemble of support vector machines. When tested on the dataset BCE-Arabic [Saad et al. in: ACM 9th annual international conference on pervasive technologies related to assistive environments (PETRA’16), Corfu, 2016], ECDP yielded an average patch classification accuracy of 97.3% and average \(F_1\) score of 95.26% for text patches and efficiently extracted text zones in both paragraphs and text-embedded graphics, even if the text is rotated by \(90^{\circ }\) or is in English. ECDP outperforms a classical layout analysis method (RLSA) and a state-of-the-art commercial product (RDI-CleverPage) on this dataset and maintains a relatively high level of performance on document images drawn from two other datasets (Hesham et al. in Pattern Anal Appl 20:1275–1287, 2017; Proprietary Dataset of 109 Arabic Documents. http://www.rdi-eg.com). The results suggest that the proposed method has the potential to generalize well to the analysis of documents with a broad range of content.
      PubDate: 2018-04-02
      DOI: 10.1007/s10032-018-0298-x
       
  • Text and non-text separation in offline document images: a survey
    • Authors: Showmik Bhowmik; Ram Sarkar; Mita Nasipuri; David Doermann
      Abstract: Abstract Separation of text and non-text is an essential processing step for any document analysis system. Therefore, it is important to have a clear understanding of the state-of-the-art of text/non-text separation in order to facilitate the development of efficient document processing systems. This paper first summarizes the technical challenges of performing text/non-text separation. It then categorizes offline document images into different classes according to the nature of the challenges one faces, in an attempt to provide insight into various techniques presented in the literature. The pros and cons of various techniques are explained wherever possible. Along with the evaluation protocols, benchmark databases, this paper also presents a performance comparison of different methods. Finally, this article highlights the future research challenges and directions in this domain.
      PubDate: 2018-03-08
      DOI: 10.1007/s10032-018-0296-z
       
  • Attribute CNNs for word spotting in handwritten documents
    • Authors: Sebastian Sudholt; Gernot A. Fink
      Abstract: Abstract Word spotting has become a field of strong research interest in document image analysis over the last years. Recently, AttributeSVMs were proposed which predict a binary attribute representation (Almazán et al. in IEEE Trans Pattern Anal Mach Intell 36(12):2552–2566, 2014). At their time, this influential method defined the state of the art in segmentation-based word spotting. In this work, we present an approach for learning attribute representations with convolutional neural networks(CNNs). By taking a probabilistic perspective on training CNNs, we derive two different loss functions for binary and real-valued word string embeddings. In addition, we propose two different CNN architectures, specifically designed for word spotting. These architectures are able to be trained in an end-to-end fashion. In a number of experiments, we investigate the influence of different word string embeddings and optimization strategies. We show our attribute CNNs to achieve state-of-the-art results for segmentation-based word spotting on a large variety of data sets.
      PubDate: 2018-02-14
      DOI: 10.1007/s10032-018-0295-0
       
  • Efficient document image binarization using heterogeneous computing and
           parameter tuning
    • Authors: Florian Westphal; Håkan Grahn; Niklas Lavesson
      Abstract: Abstract In the context of historical document analysis, image binarization is a first important step, which separates foreground from background, despite common image degradations, such as faded ink, stains, or bleed-through. Fast binarization has great significance when analyzing vast archives of document images, since even small inefficiencies can quickly accumulate to years of wasted execution time. Therefore, efficient binarization is especially relevant to companies and government institutions, who want to analyze their large collections of document images. The main challenge with this is to speed up the execution performance without affecting the binarization performance. We modify a state-of-the-art binarization algorithm and achieve on average a 3.5 times faster execution performance by correctly mapping this algorithm to a heterogeneous platform, consisting of a CPU and a GPU. Our proposed parameter tuning algorithm additionally improves the execution time for parameter tuning by a factor of 1.7, compared to previous parameter tuning algorithms. We see that for the chosen algorithm, machine learning-based parameter tuning improves the execution performance more than heterogeneous computing, when comparing absolute execution times.
      PubDate: 2018-01-13
      DOI: 10.1007/s10032-017-0293-7
       
  • Handling noise in textual image resolution enhancement using online and
           offline learned dictionaries
    • Authors: Rim Walha; Fadoua Drira; Frank Lebourgeois; Christophe Garcia; Adel M. Alimi
      Abstract: Abstract The resolution enhancement of textual images poses a significant challenge mainly in the presence of noise. The inherent difficulties are twofold. First is the reconstruction of an upscaled version of the input low-resolution image without amplifying the effect of noise. Second is the achievement of an improved visual image quality and a better OCR accuracy. Classically, the issue is addressed by the application of a denoising step used as a preprocessing or a post-processing to the magnification process. Starting by a denoising process could be more promising to avoid any magnified artifacts while proceeding otherwise. However, the state of the art underlines the limitations of denoising approaches faced with the low spatial resolution of textual images. Recently, sparse coding has attracted increasing interest due to its effectiveness in different reconstruction tasks. This study proves that the application of an efficient sparse coding-based denoising process followed by the magnification process can achieve good restoration results even if the input image is highly noisy. The main specificities of the proposed sparse coding-based framework are: (1) cascading denoising and magnification of each image patch, (2) the use of sparsity stemmed from the non-local self-similarity given in textual images and (3) the use of dual dictionary learning involving both online and offline dictionaries that are selected adaptively for each local region of the input degraded image to recover its corresponding noise-free high-resolution version. Extensive experiments on synthetic and real low-resolution noisy textual images are carried out to validate visually and quantitatively the effectiveness of the proposed system. Promising results, in terms of image visual quality as well as character recognition rates, are achieved when compared it with the state-of-the-art approaches.
      PubDate: 2018-01-11
      DOI: 10.1007/s10032-017-0294-6
       
  • Character segmentation and transcription system for historical Japanese
           books with a self-proliferating character image database
    • Authors: Chulapong Panichkriangkrai; Liang Li; Takaaki Kaneko; Ryo Akama; Kozaburo Hachimura
      Pages: 241 - 257
      Abstract: Abstract This paper describes an interactive system for assisting transcription work for digitized historical woodblock-printed Japanese books published in the seventeenth to nineteenth centuries. The main functions of the system include layout analysis, character segmentation, transcription, and the generation of a character image database. The procedures for using the system consist of two major phases. In the first phase, the system automatically produces provisional character segmentation data, and users interactively edit the segmentation results and transcribe them into text data. Information obtained in this phase is stored in the character image database. In the second phase, the system performs automatic character segmentation and transcription by using the database generated in the first phase. Through repeated applications of these two phases to a variety of materials, the contents of the character image database will be enhanced, and the system’s performance in character segmentation and transcription will increase accordingly. Since the scheme looks like the fact that the parents produce their children and the children produce grandchildren and so on, successively, this database is called as self-proliferating database. The experiment showed that when the number of character images in the database increased, the transcription accuracy also increased accordingly. In the experiment, when the size of the database increased to 37,000, the segmentation accuracy reached 83.7%, whereas the transcription accuracy reached 69.1%.
      PubDate: 2017-12-01
      DOI: 10.1007/s10032-017-0292-8
      Issue No: Vol. 20, No. 4 (2017)
       
  • A restoration method for distorted comics to improve comic contents
           identification
    • Authors: Sang-Hoon Lee; Doyoung Kim; Sagar Jadhav; Sanghoon Lee
      Abstract: Abstract In recent years, copyright violations due to the illegal copying and distribution of e-comic contents have become an important issue. Although such violations can be rapidly and reliably detected by fingerprinting techniques, scanning introduces photometric and geometric distortions. This paper presents a restoration framework for reducing photometric and geometric distortions in copied comics to improve their content identification. First, the photometric distortion is reduced by conventional homomorphic filtering with histogram equalization. Next, the corner detection is improved by a consecutive pixel difference method. Once the corners are obtained for each page, the geometrical distortion is rectified by a perspective transformation method. When two adjacent pages are coupled, as often occurs in scanned and camera-captured comics, they are separated by a simple low-energy point method. The distortion-reducing performance is measured in terms of identification accuracy using the Hamming distance. In a simulation study, our proposed scheme improved the average fingerprint identification accuracy by more than 30 percentage point for single-page comics and by 28 percentage point for double-page comics, relative to conventional methods. We also analyzed the execution time of distortion reduction and page separation in low-, medium- and high-resolution images. Even for large images, the average processing time of our scheme was within 2 s, which is a sufficiently short time for commercial applications.
      PubDate: 2017-11-24
      DOI: 10.1007/s10032-017-0291-9
       
  • Recognition of handwritten Lanna Dhamma characters using a set of
           optimally designed moment features
    • Authors: Papangkorn Inkeaw; Phasit Charoenkwan; Hui-Ling Huang; Sanparith Marukatat; Shinn-Ying Ho; Jeerayut Chaijaruwanich
      Abstract: Abstract Lanna Dhamma alphabet was used mainly for religious communication in the ancient Lanna Kingdom of Thailand. The old manuscripts using this alphabet are gradually decayed. It is desirable to preserve these valuable manuscripts in machine-encoded text files. Existing works used optical character recognition (OCR) methods based on wavelet transform for recognition of handwritten Lanna Dhamma characters. However, the test accuracy of writer-independent recognition is not satisfactory. This work proposes an OCR method, called LDIMS, for recognition of handwritten Lanna Dhamma characters using a set of optimally designed moment features. The LDIMS using an optimization approach to feature selection consists of three main phases: (1) determination of moment orders for each of eight effective moment descriptors, (2) the best combination of selected moment descriptors and (3) the optimized selection of moment features using an inheritable bi-objective genetic algorithm. The LDIMS has three individual feature sets for the recognition of handwritten Lanna Dhamma characters in upper, middle and lower levels. The character images gleaned from previous work were used as a training dataset. A new character image dataset from different writers was established for evaluating ability of writer-independent recognition. The experimental results show that the LDIMS using four moment descriptors, Meixner, Charlier, Tchebichef and Hahn, has test accuracies of 86.60, 74.38 and 85.82% for the characters in upper, middle and lower levels, respectively. The LDIMS with a mean accuracy of 82.27% performed well in recognizing the handwritten Lanna Dhamma characters from new writers, compared to existing methods using generic descriptors in terms of both accuracy and feature number used. Experimental results show that the generalized OCR method, LDIMS, is also effective for character recognition of digit and English alphabets, compared to existing methods.
      PubDate: 2017-10-12
      DOI: 10.1007/s10032-017-0290-x
       
  • On writer identification for Arabic historical manuscripts
    • Authors: Abedelkadir Asi; Alaa Abdalhaleem; Daniel Fecker; Volker Märgner; Jihad El-Sana
      Abstract: Abstract This paper introduces new methodologies for reliably identifying writers of Arabic historical manuscripts. We propose an approach that transforms key point-based features, such as SIFT, into a global form that captures high-level characteristics of writing styles. We suggest a modification for a common local feature, the contour direction feature, and show the contribution of combining local and global features for writer identification. Our work also presents a novel algorithm that determines the number of writers involved in writing a given manuscript. The experimental study confirms the significant improvement in this algorithm on writer identification once applied to historical manuscripts. Comprehensive experiments using different features and classification schemes demonstrate the vitality of the suggested methodologies for reliable writer identification. The presented techniques were evaluated on both historical and modern documents where the suggested features yielded very promising results with respect to state-of-the-art features.
      PubDate: 2017-08-01
      DOI: 10.1007/s10032-017-0289-3
       
  • Post-processing coding artefacts for JPEG documents
    • Authors: The-Anh Pham; Mathieu Delalandre
      Abstract: Abstract Coding artefacts, including ringing and blocking artefacts, are often introduced when document images are compressed using the JPEG standard. These artefacts severely impact visual perception of the image content. Although a number of methods have been presented to deal with coding artefacts, most of them are dedicated to natural images; few works have investigated to work on document content. The current work is an attempt to fill this lack. In contrast to all the approaches taken by previous works, we propose to post-process the coding artefacts by estimating the quantization noise, which is not available on the decoder’s side. The estimated noise is then used to reconstruct the image with better quality. A number of experiments were conducted to show the efficiency of the proposed method in comparison with the state-of-the-art methods.
      PubDate: 2017-06-29
      DOI: 10.1007/s10032-017-0288-4
       
  • A sigma-lognormal model-based approach to generating large synthetic
           online handwriting sample databases
    • Authors: Ujjwal Bhattacharya; Réjean Plamondon; Souvik Dutta Chowdhury; Pankaj Goyal; Swapan K. Parui
      Abstract: Abstract This article describes a methodology to generate a large database of synthetic samples from a small set of original online handwriting specimens. The overall paradigm is based on the Kinematic Theory of rapid human movements and its sigma-lognormal model. The principal contributions of the present study include (i) development of a strategy for sigma-lognormal model-based generation of synthetic samples from real online handwriting samples of arbitrary scripts captured by arbitrary relevant devices and (ii) verification of the structural similarities, including the naturalness of such synthetic prototypes, through various human perception experiments, computer evaluations and statistical hypothesis testing. A database consisting of a large number of online synthetic handwritten word samples is used to train and evaluate the performance of three existing automatic online handwriting recognition systems. Training based on a combined set of original and synthetic samples improves the recognition accuracies on the test set. A combined training set is useful irrespective of the nature of the feature set used (online, offline or combined). Although the proposed method has primarily been developed and applied to the design of an online handwriting sample database of a popular Indian script, Bangla, it can be applied to the generation of large databases of any arbitrary script for example: English, Chinese and Arabic.
      PubDate: 2017-05-26
      DOI: 10.1007/s10032-017-0287-5
       
  • A multi-one-class dynamic classifier for adaptive digitization of document
           streams
    • Authors: Anh Khoi Ngo Ho; Véronique Eglin; Nicolas Ragot; Jean-Yves Ramel
      Abstract: Abstract In this paper, we present a new dynamic classifier design based on a set of one-class independent SVM for image data stream categorization. Dynamic or continuous learning and classification has been recently investigated to deal with different situations, like online learning of fixed concepts, learning in non-stationary environments (concept drift) or learning from imbalanced data. Most of solutions are not able to deal at the same time with many of these specificities. Particularly, adding new concepts, merging or splitting concepts are most of the time considered as less important and are consequently less studied, whereas they present a high interest for stream-based document image classification. To deal with that kind of data, we explore a learning and classification scheme based on one-class SVM classifiers that we call mOC-iSVM (multi-one-class incremental SVM). Even if one-class classifiers are suffering from a lack of discriminative power, they have, as a counterpart, a lot of interesting properties coming from their independent modeling. The experiments presented in the paper show the theoretical feasibility on different benchmarks considering addition of new classes. Experiments also demonstrate that the mOC-iSVM model can be efficiently used for tasks dedicated to documents classification (by image quality and image content) in a context of streams, handling many typical scenarii for concepts extension, drift, split and merge.
      PubDate: 2017-05-18
      DOI: 10.1007/s10032-017-0286-6
       
  • Mean-Shift segmentation and PDE-based nonlinear diffusion: toward a common
           variational framework for foreground/background document image
           segmentation
    • Authors: Fadoua Drira; Frank LeBourgeois
      Abstract: Abstract The presence of noise in images of degraded documents limits the direct application of segmentation approaches and can lead to the presence of a number of different artifacts in the final segmented image. A possible solution is the integration of a pre-filtering step which may improve the segmentation quality through the reduction of such noise. This study demonstrated that combining the Mean-Shift clustering algorithm and the tensor-driven diffusion process into a joint iterative framework produced promising results. For instance, this framework generates segmented images with reduced edge and background artifacts when compared to results obtained after applying each method separately. This improvement is explained by the mutual interaction of global and local information, introduced, respectively, by the Mean-Shift and the anisotropic diffusion. Another point of note is that the anisotropic diffusion process smoothed images while preserving edge continuities. The convergence of this framework was defined automatically under a stopping criterion not previously defined when the diffusion process was applied alone. To obtain a fast convergence, the common framework utilizes the speedup algorithm of the Fukunaga and Hostetler Mean-Shift formulation already proposed by Lebourgeois et al. (International Conference on Document Analysis and Recognition (ICDAR), pp 52–56, 2013). This new variant of the Mean-Shift algorithm produced similar results to the original one, but ran faster due to the application of the integral volume. The first application of this framework was document ink bleed-through removal where noise is stemmed from the interference of the verso side on the recto side, thus perturbing the legibility of the original text. Other categories of images could also be subjected to the proposed framework application.
      PubDate: 2017-05-15
      DOI: 10.1007/s10032-017-0285-7
       
 
 
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