Schomaker, prof. dr. Lambert
Lambert Schomaker is hoogleraar Kunstmatige Intelligentie en wetenschappelijk directeur van het onderzoeksinstituut ALICE (Artificial Intelligence & Cognitive Engineering). Schomaker heeft zijn naam verbonden aan diverse projecten waarin in schuinschrift geschreven teksten online worden geanalyseerd op basis van de kennis over de bewegingen die bij het schrijven worden gemaakt. Op dit moment werkt hij aan een aantal projecten op het gebied van verwerking op basis van afbeeldingen, online en offline handschriftherkenning, forensische auteursherkenning en cognitieve robotnavigatiemodellen. Zijn werk op het gebied van neurale netwerken voor handschrift- en gebaarherkenning was een voorloper van de hedendaagse methoden van handschrift- en gebaarherkenning die op tabletcomputers zoals de iPad worden gebruikt.
Momenteel werkt hij aan een project (Target) op het gebied van grootschalige opslag, supercomputers en datamining, aan de hand waarvan de generieke zoekmachine Monk voor handgeschreven historische archieven kan worden geïmplementeerd. Het systeem achter Monk is wereldwijd uniek dankzij zijn enorme schaal, genericiteit en het gebruik van ‘live’ , '24/7', machinaal leren. Binnen een ander project, genaamd Mantis, is Schomaker bezig met het ontwikkelen van slimme systemen die problemen in productielijnen kunnen opsporen en oplossen. Schomaker ontwikkelt op basis van de robuustheidsbeginselen uit de Kunstmatige Intelligentie slimme systemen die problemen in productielijnen kunnen opsporen en oplossen.
In 2021 ontdekten prof. Mladen Popović (expert op het gebied van de Dode Zeerollen), promovendus Maruf Dhali en Schomaker, door analyse van het handschrift met kunstmatige intelligentie, dat de beroemde grote Jesaja-rol door twee schrijvers is geschreven.
In 2023 kreeg het project HAICu, waar Schomaker penvoerder vanuit de RUG van is, een subsidie van 10,3 miljoen euro van de Nationale Wetenschapsagenda. In het onderzoeksproject werken AI- en Digital Humanities-onderzoekers samen met erfgoedprofessionals en geïnteresseerde burgers aan wetenschappelijke doorbraken om grootschalige digitale erfgoedcollecties te ontsluiten, te koppelen en te analyseren.
Eerder in het nieuws
2024
Chen,
Y., Schomaker, L., & Cruz, F. (2024).
Boosting Reinforcement Learning Algorithms in Continuous
Robotic Reaching Tasks using Adaptive Potential
Functions. arXiv. https://doi.org/10.48550/arXiv.2402.04581
Cipollini,
D., Profumo, F., Schomaker, L., Milani, P.,
& Borghi, F. (2024). Conduction mechanisms in a planar
nanocomposite resistive switching device based on cluster-assembled
Au/ZrOx films. Frontiers in
Materials, 11, Article 1385792. https://doi.org/10.3389/fmats.2024.1385792
Ogum,
B., Schomaker, L., & Carloni, R.
(2024). Learning to Walk With Deep Reinforcement Learning:
Forward Dynamic Simulation of a Physics-Based Musculoskeletal Model
of an Osseointegrated Transfemoral Amputee: Forward Dynamic
Simulation of a Physics-Based Musculoskeletal Model of an
Osseointegrated Transfemoral Amputee. IEEE
Transactions on Neural Systems and Rehabilitation
Engineering, 32, 431-441. https://doi.org/10.1109/TNSRE.2024.3352416
Cipollini,
D., Swierstra, A., & Schomaker, L. (2024).
Modeling a domain wall network in BiFeO3 with
stochastic geometry and entropy-based similarity measure.
Frontiers in Materials, 11, Article
1323153. https://doi.org/10.3389/fmats.2024.1323153
Zhang,
Z., & Schomaker, L. (2024).
Optimizing and interpreting the latent space of the
conditional text-to-image GANs. Neural Computing
and Applications, 36(5), 2549–2572. https://doi.org/10.1007/s00521-023-09185-6
Dhali,
M. A., Reynolds, T., Alizadeh, A. Z., Nijdam, S. H.,
& Schomaker, L. (2024). Pattern Recognition
Techniques in Image-Based Material Classification
of Ancient Manuscripts. In Lecture Notes in
Computer Science: Pattern Recognition Applications and Methods
(Vol. 14547, pp. 124–150). Springer. https://doi.org/10.1007/978-3-031-54726-3_8
Koopmans,
L., Dhali, M. A., & Schomaker, L.
(2024). Performance Analysis of Handwritten Text Augmentation
on Style-Based Dating of Historical Documents. SN
Computer Science, 5, Article 397. https://doi.org/10.1007/s42979-024-02688-6
Bosch,
N., Okafor, E., Vriens, M., & Schomaker, L.
(2024). Predicting maintenance costs of an IT system using
artificial intelligence models. Applied Marketing
Analytics, 10(1), 68-76. https://doi.org/10.69554/csvo2679
Cipollini,
D., & Schomaker, L. (2024).
Thermodynamic and algorithmic efficiency in heterogeneous
networks. arXiv.
Cipollini,
D., & Schomaker, L. (2024). Tree
networks of real-world data: analysis of efficiency and
spatiotemporal scales. arXiv. https://doi.org/10.48550/arXiv.2404.17829
2023
Cipollini,
D., & Schomaker, L. (2023).
Conduction and entropy analysis of a mixed memristor-resistor
model for neuromorphic networks. Neuromorphic
computing and engineering, 3(3), Article
acd6b3. https://doi.org/10.1088/2634-4386/acd6b3
Ameryan,
M., & Schomaker, L. (2023).
Correction to: A Limited-size ensemble of homogeneous
CNN/LSTMS for high-performance word classification.
Neural Computing and Applications,
35, 20443–20444. https://doi.org/10.1007/s00521-023-08855-9
Zhang,
Z., & Schomaker, L. (2023).
Fusion-S2iGan: An Efficient and Effective Single-Stage
Framework for Speech-to-Image Generation. arXiv. https://doi.org/10.48550/arXiv.2305.10126
Ameryan,
M., & Schomaker, L. (2023). How to
limit label dissipation in neural-network validation: Exploring
label-free early-stopping heuristics. Journal on
Computing and Cultural Heritage, 16(1), 1-20.
Article 2. https://doi.org/10.1145/3587168
Reynolds,
T., Dhali, M., & Schomaker, L.
(2023). Image-Based Material Analysis of Ancient Historical
Documents. In Proceedings of the 12th International
Conference on Pattern Recognition Applications and Methods -
ICPRAM (Vol. 1, pp. 697-706). SciTePress. https://doi.org/10.5220/0011743700003411
Luo,
S., & Schomaker, L. (2023). Multiple
Subgoals-guided Hierarchical Learning in Robot Navigation.
In 2022 IEEE International Conference on Robotics and
Biomimetics, ROBIO 2022 (pp. 9-14). (2022 IEEE International
Conference on Robotics and Biomimetics, ROBIO 2022). Institute of
Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ROBIO55434.2022.10011912
Wenniger,
G. M. D. B., Dongen, T. V., & Schomaker, L.
(2023). MultiSChuBERT: Effective Multimodal Fusion for
Scholarly Document Quality Prediction. arXiv. https://doi.org/10.48550/arXiv.2308.07971
Schomaker,
L., Timmermans, J., & Banerjee,
T. (2023). Non-linear adaptive control inspired by
neuromuscular systems. Bioinspiration &
biomimetics, 18(4), Article 046015. https://doi.org/10.1088/1748-3190/acd896
Anteghini,
M., Haja, A., Martins Dos Santos, V. A. P.,
Schomaker, L., & Saccenti, E. (2023). OrganelX web
server for sub-peroxisomal and sub-mitochondrial protein
localization and peroxisomal target signal detection.
Computational and Structural Biotechnology
Journal, 21, 128-133. https://doi.org/10.1016/j.csbj.2022.11.058
Haja,
A., van der Woude, B., & Schomaker, L.
(2023). Organoids Segmentation using Self-Supervised
Learning: How Complex Should the Pretext Task Be? In
ICBBE 2023 - Proceedings of the 2023 10th International
Conference on Biomedical and Bioinformatics Engineering (pp.
17-27). (ACM International Conference Proceeding Series). ACM Press
Digital Library. https://doi.org/10.1145/3637732.3637772
Luo,
S., & Schomaker, L. (2023).
Reinforcement learning in robotic motion planning by combined
experience-based planning and self-imitation learning.
Robotics and Autonomous Systems,
170, Article 104545. https://doi.org/10.1016/j.robot.2023.104545
Haja,
A., Zhelev, I., & Schomaker, L. (2023).
Segmentation Of Organoid Cultures Images Using Diffusion
Networks with Triplet Loss. In Proceedings of 2023 10th
International Conference on Biomedical and Bioinformatics
Engineering (ICBBE 2023) (pp. 1-10). ACM Press. https://doi.org/10.1145/3637732.3637770
Haja,
A., Brouwer, E., & Schomaker,
L. (2023). Self-Supervised Versus Supervised
Training for Segmentation of Organoid Images. arXiv. https://doi.org/10.48550/arXiv.2311.11198
Koopmans,
L., Dhali, M., & Schomaker, L.
(2023). The Effects of Character-Level Data Augmentation on
Style-Based Dating of Historical Manuscripts. In
Proceedings of the 12th International Conference on Pattern
Recognition Applications and Methods - ICPRAM (Vol. 1, pp.
124-135). SciTePress. https://doi.org/10.5220/0011699500003411
Haja,
A., Horcas-Nieto, J. M., Bakker, B.
M., & Schomaker, L. (2023). Towards
automatization of organoid analysis: A deep learning approach to
localize and quantify organoid images. Computer
Methods and Programs in Biomedicine Update, 3,
Article 100101. https://doi.org/10.1016/j.cmpbup.2023.100101
2022
Rieck,
J. L., Cipollini, D., Salverda,
M., Quinteros, C. P., Schomaker, L. R.
B., & Noheda, B. (2023). Ferroelastic
Domain Walls in BiFeO3 as Memristive Networks.
Advanced Intelligent Systems, 5(1),
Article 2200292. https://doi.org/10.1002/aisy.202200292
Haja,
A., Radu, S., & Schomaker, L. (2022).
A Comparison of Different U-Net Models for Segmentation of
Overlapping Organoids. In ICBBE 2022: Proceeding of 2022
9th International Conference on Biomedical and Bioinformatics
Engineering (pp. 1-10). (ACM International Conference
Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3574198.3574199
Jacobs,
P. F., Maillette De Buy Wenniger, G., Wiering,
M., & Schomaker, L. (2022). Active
Learning for Reducing Labeling Effort in Text Classification
Tasks. In L. A. Leiva , C. Pruski , R. Markovich , A. Najjar
, & C. Schommer (Eds.), 33rd Benelux Conference on
Artificial Intelligence, BNAIC/Benelearn 2021: Esch-sur-Alzette,
Luxembourg, November 10–12, 2021, Revised Selected
Papers (pp. 3-29). (Artificial Intelligence and Machine
Learning; Vol. 1530). Springer Verlag. https://doi.org/10.1007/978-3-030-93842-0_1
Chen,
P.-H., Carmona-Garcia, J., Schomaker, L., &
Roca-Sanjuán, D. (2022). Combined QM-ML Approach to
Accelerate Photodynamics Simulation with High
Robustness. Poster session presented at 12th Congress
on Electronic Structure Principles and Applications (ESPA-2022) ,
Valencia, Spain.
Zhang,
Z., & Schomaker, L. (2022). DiverGAN:
An Efficient and Effective Single-Stage Framework for Diverse
Text-to-Image Generation.
Neurocomputing, 473, 182-198. https://doi.org/10.1016/j.neucom.2021.12.005
de
Wit, B., Schomaker, L., & Broekhuizen, J.
(2022). From Data To Control: Learning an HVAC Control
Policy. In CLIMA 2022 The 14th REHVA HVAC World
Congress https://doi.org/10.34641/clima.2022.362
Marcos
Mazon, D., Groefsema, M., Schomaker,
L., & Carloni, R. (2022). IMU-Based
Classification of Locomotion Modes, Transitions, and Gait Phases
with Convolutional Recurrent Neural Networks.
Sensors, 22, Article 8871. https://doi.org/10.3390/s22228871
Zhang,
Z., & Schomaker, L. (2022). Optimized
latent-code selection for explainable conditional text-to-image
GANs. In 2022 International Joint Conference on Neural
Networks (IJCNN) (pp. 1-9). IEEE. https://doi.org/10.1109/IJCNN55064.2022.9892738
Bleijendaal,
H., Boersma, J., Lopes, R. R., Van Der Ree, M. H., Meijborg, V. M.
F., Amin, A. S., Winter, M. M., Marquering, H., Schomaker,
L., Zwinderman, A. H., Pinto, Y. M., Wilde, A. A. M., &
Postema, P. G. (2022). PO-631-03 DEEP LEARNING FOR THE
IDENTIFICATION OF PATIENTS WITH A HIGH RISK FOR IDIOPATHIC
VENTRICULAR FIBRILLATION. Heart
Rhythm, 19(5, Supplement), S168-S169. https://doi.org/10.1016/j.hrthm.2022.03.904
Mostard,
W., Schomaker, L., & Wiering, M.
(2022). Semantic Preserving Siamese Autoencoder for Binary
Quantization of Word Embeddings. In NLPIR 2021: 2021 5th
International Conference on Natural Language Processing and
Information Retrieval (NLPIR) (pp. 30-38). ACM Press. https://doi.org/10.1145/3508230.3508235
2021
Jacobs,
P. F., Wenniger, G. M. D. B., Wiering, M., &
Schomaker, L. (2021). Active learning for reducing
labeling effort in text classification tasks. (ArXiv).
arXiv.
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. In R. Su, Y.-D. Zhang, & H. Liu (Eds.),
Proceedings of 2021 International Conference on Medical Imaging
and Computer-Aided Diagnosis (MICAD 2021): Medical Imaging and
Computer-Aided Diagnosis (pp. 37-46). (Lecture Notes in
Electrical Engineering; Vol. 784). Springer. https://doi.org/10.1007/978-981-16-3880-0_5
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.
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),
Article 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, Article
108010. https://doi.org/10.1016/j.patcog.2021.108010
Zhang,
Z., & Schomaker, L. (2021). DTGAN:
Dual Attention Generative Adversarial Networks for Text-to-Image
Generation. In 2021 International Joint Conference on
Neural Networks (IJCNN) IEEE. https://doi.org/10.1109/IJCNN52387.2021.9533527
He,
S., & Schomaker, L. (2021). GR-RNN:
Global-context residual recurrent neural networks for writer
identification. Pattern recognition,
117, Article 107975. https://doi.org/10.1016/j.patcog.2021.107975
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). Article 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). Article
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, Article 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) Article 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). 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. Article 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). Article 9257737
(Proceedings of International Conference on Frontiers in
Handwriting Recognition, ICFHR; Vol. 2020-September). 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. In 2020 29th IEEE International
Conference on Robot and Human Interactive Communication
(RO-MAN) (pp. 781-787). IEEE. https://doi.org/10.1109/RO-MAN47096.2020.9223541
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, Article 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
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
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. arXiv. https://doi.org/10.48550/arXiv.2005.00129
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/10.48550/arXiv.1904.08421
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://doi.org/10.48550/arXiv.1911.07930
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. https://doi.org/10.1201/9781003339748-5
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.
555-567). River Publishers. https://doi.org/10.1201/9781003339748-9
Steging,
C., Schomaker, L., & Verheij,
B. (2019). The Xai paradox: Systems that perform
well for the wrong reasons. Paper presented at
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