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Schomaker, Prof. Lambert

Lambert Schomaker
Lambert Schomaker

Lambert Schomaker is professor of Artificial Intelligence and scientific director of the research institute ALICE (Artificial Intelligence & Cognitive Engineering). He has worked on several projects concerning the recognition of online, connected cursive script on the basis of knowledge of the handwriting movement process. Current projects are in the area of image-based retrieval, online and offline handwriting recognition, forensic writer identification, and cognitive robot navigation models. His work on neural networks for handwriting and gesture recognition was a precursor to modern handwriting and gesture-recognition methods on tablet computers such as the iPad.

He is currently active in a multidisciplinary project (Target) for mass-storage, high-performance computing and datamining, in order to implement the Monk generic search engine for handwritten historical archives. The Monk system is unique in the world due to its huge scale, genericity and its use of live, '24/7', machine learning. In another project (Mantis), Schomaker is using robustness principles from AI to develop smart systems that can detect and solve problems along industrial assembly lines.

In 2021, through analysis of the manuscript with artificial intelligence, Prof. Mladen Popović (expert on the Dead Sea Scrolls), PhD student Maruf Dhali and Schomaker discovered that the famous great Isaiah scroll was written by two writers.

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Publications

2021

Haja, A., & Schomaker, L. R. B. (2021). A fully automated end-to-end process for fluorescence microscopy images of yeast cells: From segmentation to detection and classification. ArXiv. http://arxiv.org/abs/2104.02793v1
Ameryan, M., & Schomaker, L. (2021). A high-performance word recognition system for the biological fieldnotes of the Natuurkundige Commissie. In A. Weber, M. Heerlien, E. Gassó Miracle, & K. Wolstencroft (Eds.), Collect and Connect: Archives and Collections in a Digital Age 2020 (pp. 92-103). (CEUR Workshop Proceedings; Vol. 2810). CEUR-WS.org.
Jacobs, P. F., Wenniger, G. M. D. B., Wiering, M., & Schomaker, L. (2021). Active learning for reducing labeling effort in text classification tasks. ArXiv.
Chen, Y., Schomaker, L., & Wiering, M. (2021). An Investigation Into the Effect of the Learning Rate on Overestimation Bias of Connectionist Q-learning. In A. P. Rocha, L. Steels, & J. van den Herik (Eds.), Proceedings of the 13th International Conference on Agents and Artificial Intelligence (Vol. 2, pp. 107-118). SciTePress. https://doi.org/10.5220/0010227301070118
Popović, M., Dhali, M. A., & Schomaker, L. (2021). Artificial intelligence based writer identification generates new evidence for the unknown scribes of the Dead Sea Scrolls exemplified by the Great Isaiah Scroll (1QIsaa). PLoS ONE, 16(4), [e0249769]. https://doi.org/10.1371/journal.pone.0249769
He, S., & Schomaker, L. (2021). CT-Net: Cascade T-shape deep fusion networks for document binarization. Pattern recognition, 118, [108010]. https://doi.org/10.1016/j.patcog.2021.108010
Zhang, Z., & Schomaker, L. (2021). DiverGAN: An Efficient and Effective Single-Stage Framework for Diverse Text-to-Image Generation. arXiv.
He, S., & Schomaker, L. (2021). GR-RNN: Global-Context Residual Recurrent Neural Networks for Writer Identification. ArXiv. http://arxiv.org/abs/2104.05036v1
Schomaker, L. (2021). Lifelong learning for text retrieval and recognition in historical handwritten document collections. In A. Fischer, M. Liwicki, & R. Ingold (Eds.), Handwritten Historical Document Analysis, Recognition, and Retrieval — State of the Art and Future Trends: Series in Machine Perception and Artificial Intelligence (Vol. 89, pp. 221-248). (Series in Machine Perception and Artificial Intelligence; Vol. 89). World Scientific Publishing. https://doi.org/10.1142/9789811203244_0012
Chanda, S., Haitink, D., Prasad, P. K., Baas, J., Pal, U., & Schomaker, L. (2021). Recognizing Bengali Word Images - A Zero-Shot Learning Perspective. In 2020 25th International Conference on Pattern Recognition (ICPR) (pp. 5603-5610). [9412607] IEEE. https://doi.org/10.1109/ICPR48806.2021.9412607
Chen, Y., Kasaei, H., Schomaker, L., & Wiering, M. (2021). Reinforcement Learning with Potential Functions Trained to Discriminate Good and Bad States. In 2021 International Joint Conference on Neural Networks (IJCNN) (pp. 1-7). [9533682] IEEE. https://doi.org/10.1109/IJCNN52387.2021.9533682
Luo, S., Kasaei, H., & Schomaker, L. (2021). Self-Imitation Learning by Planning. In 2021 IEEE International Conference on Robotics and Automation (ICRA) (pp. 4823-4829). IEEE. https://doi.org/10.1109/ICRA48506.2021.9561411
Shantia, A., Timmers, R., Chong, Y., Kuiper, C., Bidoia, F., Schomaker, L., & Wiering, M. (2021). Two-stage visual navigation by deep neural networks and multi-goal reinforcement learning. Robotics and Autonomous Systems, 138, [103731]. https://doi.org/10.1016/j.robot.2021.103731

2020

Dijkstra, K., van de Loosdrecht, J., Atsma, W. A., Schomaker, L. R. B., & Wiering, M. A. (2021). CentroidNetV2: A hybrid deep neural network for small-object segmentation and counting. Neurocomputing, 423, 490-505. https://doi.org/10.1016/j.neucom.2020.10.075
Luo, S., Kasaei, H., & Schomaker, L. (2020). Accelerating Reinforcement Learning for Reaching using Continuous Curriculum Learning. In Proceedings of 2020 International Joint Conference on Neural Networks (IJCNN) [9207427] IEEE. https://doi.org/10.1109/IJCNN48605.2020.9207427
Pawara, P., Boshchenko, A., Schomaker, L. R. B., & Wiering, M. A. (2020). Deep Learning with Data Augmentation for Fruit Counting. In L. Rutkowski, R. Scherer, M. Korytkowski, W. Pedrycz, R. Tadeusiewicz, & J. M. Zurada (Eds.), 19th International Conference, ICAISC 2020, Zakopane, Poland, October 12-14, 2020: Proceedings, Part I (pp. 203-214). ( Lecture Notes in Computer Science; Vol. 12415). Springer. https://doi.org/10.1007/978-3-030-61401-0_20
Zhang, Z., & Schomaker, L. (2020). DTGAN: Dual Attention Generative Adversarial Networks for Text-to-Image Generation. ArXiv. http://arxiv.org/abs/2011.02709v2
Dhali, M. A., Jansen, C. N., De Wit, J. W., & Schomaker, L. (2020). Feature-extraction methods for historical manuscript dating based on writing style development. Pattern Recognition Letters, 131, 413-420. https://doi.org/10.1016/j.patrec.2020.01.027
He, S., & Schomaker, L. (2020). FragNet: Writer Identification using Deep Fragment Networks. IEEE transactions on information forensics and security, 15, 3013-3022. [9040654]. https://doi.org/10.1109/TIFS.2020.2981236
Ameryan, M., & Schomaker, L. (2020). Improving the robustness of LSTMs for word classification using stressed word endings in dual-state word-beam search. In Proceedings of the 2020, 17th International Conference on Frontiers in Handwriting Recognition (ICFHR) (pp. 13-18). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/icfhr2020.2020.00014
Lu, H., Schomaker, L., & Carloni, R. (2020). IMU-based Deep Neural Networks for Locomotor Intention Prediction. In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) IEEE. https://doi.org/10.1109/IROS45743.2020.9341649
Li, Y., Schomaker, L., & Kasaei, S. H. (2020). Learning to Grasp 3D Objects using Deep Residual U-Nets. ArXiv, 781-787. https://arxiv.org/pdf/2002.03892v1
Pawara, P., Okafor, E., Groefsema, M., He, S., Schomaker, L. R. B., & Wiering, M. A. (2020). One-vs-One classification for deep neural networks. Pattern recognition, 108, [107528]. https://doi.org/10.1016/j.patcog.2020.107528
van Dongen, T., Maillette de Buy Wenniger, G., & Schomaker, L. (2020). SChuBERT: Scholarly Document Chunks with BERT-encoding boost Citation Count Prediction. In M. K. Chandrasekaran (Ed.), Proceedings of the First Workshop on Scholarly Document Processing (pp. 148-157). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2020.sdp-1.17
Maillette de Buy Wenniger, G., van Dongen, T., Aedmaa, E., Teun Kruitbosch, H., Valentijn, E. A., & Schomaker, L. (2020). Structure-Tags Improve Text Classification for Scholarly Document Quality Prediction. Manuscript submitted for publication. http://adsabs.harvard.edu/abs/2020arXiv200500129M
Wenniger, G. M. D. B., Dongen, T. V., Aedmaa, E., Kruitbosch, H. T., Valentijn, E. A., & Schomaker, L. (2020). Structure-Tags Improve Text Classification for Scholarly Document Quality Prediction. In Proceedings of the First Workshop on Scholarly Document Processing (pp. 158-167). (Proceedings of the First Workshop on Scholarly Document Processing. Association for Computational Linguistics.). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2020.sdp-1.18
Oosterhuis, T., & Schomaker, L. (2020). "Who is Driving around Me?": Unique Vehicle Instance Classification using Deep Neural Features. ArXiv. https://arxiv.org/abs/2003.08771

2019

Ameryan, M., & Schomaker, L. (2021). A limited-size ensemble of homogeneous CNN/LSTMs for high-performance word classification. Neural Computing and Applications, 33, 8615–8634. https://doi.org/10.1007/s00521-020-05612-0
Schomaker, L. (2019). A large-scale field test on word-image classification in large historical document collections using a traditional and two deep-learning methods. ArXiv. https://doi.org/10.13140/RG.2.2.11940.53120
Dhali, M. A., Wit, J. W. D., & Schomaker, L. (2019). BiNet: Degraded-Manuscript Binarization in Diverse Document Textures and Layouts using Deep Encoder-Decoder Networks. ArXiv. https://arxiv.org/pdf/1911.07930v1
Dijkstra, K., van de Loosdrecht, J., Schomaker, L. R. B., & Wiering, M. A. (2019). CentroidNet: A Deep Neural Network for Joint Object Localization and Counting. In U. Brefeld, E. Curry, E. Daly, B. MacNamee, A. Marascu, F. Pinelli, M. Berlingerio, & N. Hurly (Eds.), ECML PKDD 2018: Machine Learning and Knowledge Discovery in Databases (pp. 585-601). ( Lecture Notes in Computer Science; Vol. 11053). Springer. https://doi.org/10.1007/978-3-030-10997-4_36
He, S., & Schomaker, L. (2019). Deep adaptive learning for writer identification based on single handwritten word images. Pattern recognition, 88, 64-74. https://doi.org/10.1016/j.patcog.2018.11.003
He, S., & Schomaker, L. (2019). DeepOtsu: Document enhancement and binarization using iterative deep learning. Pattern recognition, 91, 379-390. https://doi.org/10.1016/j.patcog.2019.01.025
Sriman, B., & Schomaker, L. (2019). Multi-script text versus non-text classification of regions in scene images. Journal of Visual Communication and Image Representation, 62, 23-42. https://doi.org/10.1016/j.jvcir.2019.04.007
Wenniger, G. M. D. B., Schomaker, L., & Way, A. (2019). No Padding Please: Efficient Neural Handwriting Recognition. In 2019 International Conference on Document Analysis and Recognition (ICDAR) (pp. 355-362). IEEE. https://doi.org/10.1109/ICDAR.2019.00064
Sillitti, A., Schomaker, L., Anakabe, J. F., Basurko, J., Dam, P., Ferreira, H., Ferreiro, S., Gijsbers, J., He, S., Hegedus, C., Holenderski, M., Hooghoudt, J-O., Lecuona, I., Leturiondo, U., Marcelis, Q., Moldovan, I., Okafor, E., Rebelo de Sa, C., Romero, R., ... Zurutuza, U. (2019). Providing Proactiveness: Data Analysis Techniques Portfolios. In M. Albano, E. Jantunen, G. Papa, & U. Zurutuza (Eds.), The MANTIS Book : Cyber Physical System Based Proactive Collaborative Maintenance (pp. 145-238). River Publishers.
Schomaker, L., Albano, M., Jantunen, E., & Ferreira, L. L. (2019). The future of Maintenance. In M. Albano, E. Jantunen, G. Papa, & U. Zurutuza (Eds.), The MANTIS Book : Cyber Physical System Based Proactive Collaborative Maintenance (pp. 555). River Publishers.
Steging, C., Schomaker, L., & Verheij, B. (2019). The Xai paradox: Systems that perform well for the wrong reasons. Paper presented at BNAIC/Benelearn Conference, Brussels, Belgium.

2018

Bidoia, F., Sabatelli, M., Shantia, A., Wiering, M. A., & Schomaker, L. (2018). A Deep Convolutional Neural Network for Location Recognition and Geometry based Information. In M. De Marsico, G. Sanniti di Baja, & A. Fred (Eds.), Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods (pp. 27-36). SciTePress. https://doi.org/10.5220/0006542200270036
Okafor, E., Schomaker, L., & Wiering, M. A. (2018). An analysis of rotation matrix and colour constancy data augmentation in classifying images of animals. Journal of Information and Telecommunication, 2(4), 465-491. https://doi.org/10.1080/24751839.2018.1479932
Chanda, S., Okafor, E., Hamel, S., Stutzmann, D., & Schomaker, L. (2018). Deep Learning for Classification and as Tapped-Feature Generator in Medieval Word-Image Recognition. In 13th IAPR International Workshop on Document Analysis Systems (DAS) (pp. 217-222). IEEE. https://doi.org/10.1109/DAS.2018.82
van de Wolfshaar, J., Wiering, M., & Schomaker, L. (2018). Deep Learning Policy Quantization. In Proceedings of the 10th International Conference on Agents and Artificial Intelligence (pp. 122-130). SciTePress. https://doi.org/10.5220/0006592901220130
Okafor, E., Berendsen, G., Schomaker, L., & Wiering, M. (2018). Detection and Recognition of Badgers Using Deep Learning. In V. Kurkova, Y. Manolopoulos, B. Hammer, L. Iliadis, & I. Maglogiannis (Eds.), International Conference on Artificial Neural Networks (pp. 554-563). (Lecture Notes in Computer Science book series; Vol. 11141). Springer International Publishing, Cham, Switzerland. https://doi.org/10.1007/978-3-030-01424-7_54
Dijkstra, K., van de Loosdrecht, J., Schomaker, L. R. B., & Wiering, M. A. (2018). Hyperspectral demosaicking and crosstalk correction using deep learning. Machine Vision and Applications, 30(1). https://doi.org/10.1007/s00138-018-0965-4
Okafor, E., & Schomaker, L. (2018). Integrated Dimensionality Reduction and Sequence Prediction using LSTM. Poster session presented at ICT.Open, Amersfoort, Netherlands.
He, S., & Schomaker, L. (2018). Open Set Chinese Character Recognition using Multi-typed Attributes. ArXiv. https://arxiv.org/pdf/1808.0899
Weber, A., Ameryan, M., Wolstencroft, K., Stork, L., Heerlien, M., & Schomaker, L. (2018). Towards a Digital Infrastructure for Illustrated Handwritten Archives. In M. Ioannides (Ed.), Lecture Notes in Computer Science, vol. 10605: Final Conference of the Marie Skłodowska-Curie Initial Training Network for Digital Cultural Heritage, ITN-DCH 2017, Olimje, Slovenia (pp. 155-166). Springer. https://doi.org/10.1007/978-3-319-75826-8_13
Chanda, S., Baas, J., Haitink, D., Hamel, S., Stutzmann, D., & Schomaker, L. (2018). Zero-shot learning based approach for medieval word recognition using deep-learned features. In Proceedings of International Conference on Frontiers in Handwriting Recognition, ICFHR (pp. 345-350). (Proceedings of International Conference on Frontiers in Handwriting Recognition, ICFHR; Vol. 2018-August). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICFHR-2018.2018.00067

2017

Dhali, M., He, S., Popovic, M., Tigchelaar, E., & Schomaker, L. (2017). A Digital Palaeographic Approach towards Writer Identification in the Dead Sea Scrolls. In Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, 693-702, 2017, Porto, Portugal (pp. 693-702) https://doi.org/10.5220/0006249706930702
He, S., & Schomaker, L. (2017). Beyond OCR: Multi-faceted understanding of handwritten document characteristics. Pattern recognition, 63, 321-333. https://doi.org/10.1016/j.patcog.2016.09.017
Okafor, E., Pawara, P., Karaaba, M., Surinta, O., Codreanu, V., Schomaker, L., & Wiering, M. (2017). Comparative study between deep learning and bag of visual words for wild-animal recognition. In 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016 (2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SSCI.2016.7850111
Pawara, P., Okafor, E., Surinta, O., Schomaker, L., & Wiering, M. (2017). Comparing Local Descriptors and Bags of Visual Words to Deep Convolutional Neural Networks for Plant Recognition. In 6th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2017) ICPRAM .
Pawara, P., Okafor, E., Surinta, O., Schomaker, L., & Wiering, M. (2017). Comparing local descriptors and bags of visualwords to deep convolutional neural networks for plant recognition. In A. Fred, M. D. De Marsico, & G. S. di Baja (Eds.), ICPRAM 2017 - Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods (pp. 479-486). SciTePress.
He, S., & Schomaker, L. (2017). Co-occurrence features for writer identification. In Proceedings of International Conference on Frontiers in Handwriting Recognition, ICFHR (pp. 78-83). (Proceedings of International Conference on Frontiers in Handwriting Recognition, ICFHR). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICFHR.2016.0027
Pawara, P., Okafor, E., Schomaker, L., & Wiering, M. (2017). Data Augmentation for Plant Classification. In Advanced Concepts for Intelligent Vision Systems (Acivs 2017) [112]
Shantia, A., Bidoia, F., Schomaker, L., & Wiering, M. (2017). Dynamic Parameter Update for Robot Navigation Systems through Unsupervised Environmental Situational Analysis. In IEEE Symposium Series on Computational Intelligence (pp. 1-7). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SSCI.2016.7850238
Dijkstra, K., van de Loosdrecht, J., Schomaker, L., & Wiering, M. (2017). Hyper-spectral frequency selection for the classification of vegetation diseases. In European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (2017 ed., pp. 483-488). ESANN.
Okafor, E., Smit, R., Schomaker, L., & Wiering, M. (2017). Operational Data Augmentation in Classifying Single Aerial Images of Animals. In IEEE International Conference on INnovations in Intelligent SysTems and Applications (INISTA), 2017 (pp. 354-360). IEEE. https://doi.org/10.1109/INISTA.2017.8001185
Valentijn, E. A., Begeman, K., Belikov, A., Boxhoorn, D. R., Brinchmann, J., McFarland, J., Holties, H., Kuijken, K. H., Verdoes Kleijn, G., Vriend, W-J., Williams, O. R., Roerdink, J. B. T. M., Schomaker, L. R. B., Swertz, M. A., Tsyganov, A., & van Dijk, G. J. W. (2017). Target and (Astro-)WISE technologies - Data federations and its applications. In Astroinformatics 2017 (pp. 333-340). (Proceedings IAU Symposium; Vol. 12, issue S325, Astroinformatics). International Astronomical Union. https://doi.org/10.1017/S1743921317000254
He, S., & Schomaker, L. (2017). Writer identification using curvature-free features. Pattern recognition, 63, 451-464. https://doi.org/10.1016/j.patcog.2016.09.044

2016

He, S., Samara, P., Burgers, J., & Schomaker, L. (2016). A Multiple-Label Guided Clustering Algorithm for Historical Document Dating and Localization. Ieee transactions on image processing, 25(11), 5252-5265. https://doi.org/10.1109/TIP.2016.2602078
Bhowmik, T. K., Parui, S. K., Roy, U., & Schomaker, L. (2016). Bangla Handwritten Character Segmentation Using Structural Features: A Supervised and Bootstrapping Approach. ACM Transactions on Asian and Low-Resource Language Information Processing, 15(4), 29:1-29:26. [29]. https://doi.org/10.1145/2890497
Schomaker, L. (2016). Caveats on Bayesian and hidden-Markov models (v2.8). Manuscript submitted for publication.
Okafor, E., Pawara, P., Karaaba, M., Surinta, O., Codreanu, V., Schomaker, L., & Wiering, M. (2016). Comparative Study Between Deep Learning and Bag of Visual Words for Wild-Animal Recognition. In IEEE Symposium Series on Computational Intelligence IEEE.
He, S., Samara, P., Burgers, J., & Schomaker, L. (2016). Discovering visual element evolutions for historical document dating. In 2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR) (pp. 7-12). IEEE (The Institute of Electrical and Electronics Engineers).
Schimbinschi, F., Schomaker, L., & Wiering, M. (2016). Ensemble methods for robust 3D face recognition using commodity depth sensors. In IEEE Symposium Series on Computational Intelligence: Symposium on Computational Intelligence in Biometrics and Identity Management (Proceedings - 2015 IEEE Symposium Series on Computational Intelligence, SSCI 2015). IEEE (The Institute of Electrical and Electronics Engineers). https://doi.org/10.1109/SSCI.2015.36
He, S., & Schomaker, L. (2016). General Pattern Run-Length Transform for Writer Identification. In Proceedings - 12th IAPR International Workshop on Document Analysis Systems, DAS 2016 (pp. 60-65). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DAS.2016.42
Sriman, B., Schomaker, L., & Pruksasri, P. (2016). General Text-Chunk Localization in Scene Images using a Codebook-based Classifier. In Proceedings of the 4th IIAE International Conference on Intelligent Systems and Image Processing 2016 (pp. 134-141). IEEE. http://www2.ia-engineers.org/icisip2016/?utm_source=researchbib
He, S., Samara, P., Burgers, J., & Schomaker, L. (2016). Historical Document Dating Using Unsupervised Attribute Learning. In 2016 12th IAPR Workshop on Document Analysis Systems (DAS) (pp. 36-44). IEEE (The Institute of Electrical and Electronics Engineers).
He, S., Samara, P., Burgers, J., & Schomaker, L. (2016). Historical manuscript dating based on temporal pattern codebook. Computer Vision and Image Understanding, 152, 167-175. https://doi.org/10.1016/j.cviu.2016.08.008
He, S., Samara, P., Burgers, J., & Schomaker, L. (2016). Image-based historical manuscript dating using contour and stroke fragments. Pattern recognition, 58, 159-171. https://doi.org/10.1016/j.patcog.2016.03.032
Niitsuma, M., Schomaker, L., van Oosten, J-P., Tomita, Y., & Bell, D. (2016). Musicologist-driven writer identification in early music manuscripts. Multimedia Tools and Applications, 75(11), 6463-6479. https://doi.org/10.1007/s11042-015-2583-8

2015

Schomaker, L. (2016). Design considerations for a large-scale image-based text search engine in historical manuscript collections. Information Technology, 58(2), 80-88. https://doi.org/10.1515/itit-2015-0049
He, S., & Schomaker, L. (2015). A Polar Stroke Descriptor for Classification of Historical Documents. In 13th International Conference on Document Analysis and Recognition (ICDAR) (pp. 6-10). IEEE (The Institute of Electrical and Electronics Engineers).
Sriman, B., & Schomaker, L. (2015). Explicit Foreground and Background Modeling in The Classification of Text Blocks in Scene Images. In Proceedings of a meeting held 3-6 November 2015, Kuala Lumpur, Malaysia (Vol. 1, pp. 830). [234] IEEE. http://www.proceedings.com/30643.html
Shantia, A., Timmers, R., Schomaker, L., & Wiering, M. (2015). Indoor Localization by Denoising Autoencoders and Semi-supervised Learning in 3D Simulated Environment. In International Joint Conference on Neural Networks IEEE (The Institute of Electrical and Electronics Engineers). https://doi.org/10.1109/IJCNN.2015.7280715
Karaaba, M., Surinta, O., Schomaker, L., & Wiering, M. (2015). In-Plane Rotational Alignment of Faces by Eye and Eye-Pair Detection. In 11th International Conference on Computer Vision Theory and Applications
He, S., Wiering, M., & Schomaker, L. (2015). Junction detection in handwritten documents and its application to writer identification. Pattern recognition, 48(12), 4036-4048. https://doi.org/10.1016/j.patcog.2015.05.022
Sriman, B., & Schomaker, L. (2015). Object Attention Patches for Text Detection and Recognition in Scene Images using SIFT. In M. De Marsico, M. Figueiredo, & A. Fred (Eds.), In Proceedings of the International Conference on Pattern Recognition Applications and Methods: ICPRAM 2015 (Vol. 1, pp. 304-311). SciTePress. https://doi.org/10.5220/0005218603040311
Surinta, O., Karaaba, M. F., Schomaker, L. R. B., & Wiering, M. A. (2015). Recognition of handwritten characters using local gradient feature descriptors. Engineering Applications of Artificial Intelligence, 45, 405-414. https://doi.org/10.1016/j.engappai.2015.07.017
Surinta, O., Karaaba, M., Mishra, T. K., Schomaker, L., & Wiering, M. (2015). Recognizing Handwritten Characters with Local Descriptors and Bags of Visual Words. In L. Iliadis, & C. Jayne (Eds.), Engineering Applications of Neural Networks (EANN): 16th International Conference on Engineering Applications of Neural Networks, Proceedings (pp. 255-264). (Communications in computer and information science; Vol. 517). Springer.
Karaaba, M., Surinta, O., Schomaker, L., & Wiering, M. (2015). Robust Face Recognition by Computing Distances from Multiple Histograms of Oriented Gradients. In IEEE Symposium Series on Computational Intelligence: Symposium on Computational Intelligence in Biometrics and Identity Management IEEE (The Institute of Electrical and Electronics Engineers).

2014

Surinta, O., Karaaba, M., van Oosten, J-P., Schomaker, L., & Wiering, M. (2014). A* Path Planning for Line Segmentation of Handwritten Documents. In International Conference on Frontiers in Handwriting Recognition (ICFHR) (pp. 175-180). IEEE (The Institute of Electrical and Electronics Engineers). https://doi.org/10.1109/ICFHR.2014.37
van Oosten, J-P., & Schomaker, L. (2014). A Reevaluation and Benchmark of Hidden Markov Models. In 14th International Conference on Frontiers in Handwriting Recognition (pp. 531-536). [95] https://doi.org/10.1109/ICFHR.2014.95
He, S., & Schomaker, L. (2014). Delta-n Hinge: Rotation-invariant features for writer identification. In 22th International Conference on Pattern Recognition(ICPR) (pp. 2023-2028). IEEE (The Institute of Electrical and Electronics Engineers). https://doi.org/10.1109/ICPR.2014.353
Codreanu, V., Droge, B., Williams, D., Yasar, B., Yang, F., Liu, B., Dong, F., Surinta, O., Schomaker, L., Roerdink, J., & Wiering, M. (2014). Evaluating automatically parallelized versions of the support vector machine. Concurrency and Computation, 28(7), 2274-2294. https://doi.org/10.1002/cpe.3413
Karaaba, M. F., Schomaker, L., & Wiering, M. (2014). Machine learning for multi-view eye-pair detection. Engineering Applications of Artificial Intelligence, 33, 69-79. https://doi.org/10.1016/j.engappai.2014.04.008
Wiering, M., & Schomaker, L. (2014). Multi-Layer Support Vector Machines. In Regularization, Optimization, Kernels, and Support Vector Machines: Edition: CRC Machine Learning and Pattern Recognition Series (pp. 457-476). [20] Chapman & Hall/CRC Press.
van Oosten, J-P., & Schomaker, L. (2014). Separability versus prototypicality in handwritten word-image retrieval. Pattern recognition, 47(3), 1031-1038. https://doi.org/10.1016/j.patcog.2013.09.006
He, S., Samara, P., Burgers, J., & Schomaker, L. (2014). Towards style-based dating of historical documents. In 14th International Conference on Frontiers in Handwritten Recognition IEEE. https://doi.org/10.1109/ICFHR.2014.52

2013

Surinta, O., Schomaker, L., & Wiering, M. (2013). A Comparison of Feature and Pixel-Based Methods for Recognizing Handwritten Bangla Digits. In International Conference on Document Analysis and Recognition (ICDAR) (pp. 165-169). IEEE (The Institute of Electrical and Electronics Engineers). https://doi.org/10.1109/ICDAR.2013.40
Wiering, M., Schutten, M., Millea, A., Meijster, A., & Schomaker, L. (2013). Deep Support Vector Machines for Regression Problems. In International Workshop on Advances in Regularization, Optimization, Kernel Methods, and Support Vector Machines: theory and applications (pp. 53-54).
van der Zant, T., Kouw, M., & Schomaker, L. (2013). Generative Artificial Intelligence: Philosophy and Theory of Artificial Intelligence. In V. C. Mueller (Ed.), Philosophy and Theory of Artificial Intelligence (Vol. 5, pp. 107-120). (Studies in Applied Philosophy, Epistemology and Rational Ethics; Vol. 5). Springer. https://doi.org/10.1007/978-3-642-31674-6_8
Wiering, M., van der Ree, M., Embrechts, M., Stollenga, M., Meijster, A., Nolte, A., & Schomaker, L. (2013). The Neural Support Vector Machine. In The 25th Benelux Artificial Intelligence Conference (BNAIC)
Niitsuma, M., Schomaker, L., van Oosten, J-P., & Tomita, Y. (2013). Writer Identification in Old Music Manuscripts Using Contour-Hinge Feature and Dimensionality Reduction with an Autoencoder: Computer Analysis of Images and Patterns. In R. Wilson, E. Hancock, A. Bors, & W. Smith (Eds.), Computer Analysis of Images and Patterns; part 2 (pp. 555-562). (Lecture Notes in Computer Science; Vol. 8048). Springer. https://doi.org/10.1007/978-3-642-40246-3_69

2012

Ritsema van Eck, M., & Schomaker, L. (2012). Formal Semantic Modeling for Human and Machine-based Decoding of Medieval Manuscripts. In Proceedings of Digital Humanities
Surinta, O., Schomaker, L., & Wiering, M. (2012). Handwritten Character Classification using the Hotspot Feature Extraction Technique. In International Conference on Pattern Recognition Applications and Methods (ICPRAM) (pp. 261-264). INSTICC publishing. https://doi.org/10.5220/0003712002610264
van Oosten, J-P., & Schomaker, L. (2012). Separability versus Prototypicality in Handwritten Word Retrieval. In Frontiers in Handwriting Recognition (ICFHR), 2012 International Conference on (pp. 8-13). IEEE (The Institute of Electrical and Electronics Engineers). https://doi.org/10.1109/ICFHR.2012.269
Brink, A. A., Smit, J., Bulacu, M. L., & Schomaker, L. R. B. (2012). Writer identification using directional ink-trace width measurements. Pattern recognition, 45(1), 162-171. https://doi.org/10.1016/j.patcog.2011.07.005

2011

Pietersma, A-D., Schomaker, L., & Wiering, M. (2011). Kernel Learning in Support Vector Machines using Dual-Objective Optimization. In Belgian Dutch Artificial Intelligence Conference: BNAIC
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