einzigartige künstliche Intelligenz
Ein starkes Paar
Die technologieorientierte „Planet AI GmbH“ ist eine Tocherfirma der geschäftlich orientierten „Planet IS GmbH“ um den Fokus beider auf ihre Kompetenzen zu konzentrieren. Dabei entwickelt „Planet AI“ die Engine basierend auf der KI „PlanetBrain“. Mit dieser Technologie lösen die Produkte und Lösungen von Planet viele reale Aufgabenstellungen im Bereich der Text-, Audio- und Spracherkennung für den Regierungssektor, sowie den Medizin-, Versicherungs- und Finanzsektor.
Ein starkes Paar
Die technologieorientierte „Planet AI GmbH“ ist eine Tocherfirma der mehr geschäftlich oriententen „Planet IS GmbH“ um den Fokus beider auf ihre Kompetenzen zu konzentrieren. Dabei entwickelt „Planet AI“ die Engine basierend auf „PlanetBrain“. Mit dieser Technologie lösen die Planet-Produkte und -lösungen viele reale Aufgeabenstellungen im Bereich der Text-, Audio- und Spracherkennung für den Brachen- und Verbrauchermarkt.
Wissenschaftliche Veröffentlichungen
Encoder-decoder models have become an effective approach for sequence learning tasks like machine translation, image captioning and speech recognition, but have yet to show competitive results for handwritten text recognition. To this end, we propose an attention-based sequence-to-sequence model. It combines a convolutional neural network as a generic feature extractor with a recurrent neural network to encode both the visual information, as well as the temporal context between characters in the input image, and uses a separate recurrent neural network to decode the actual character sequence. We make experimental comparisons between various attention mechanisms and positional encodings, in order to find an appropriate alignment between the input and output sequence. The model can be trained end-to-end and the optional integration of a hybrid loss allows the encoder to retain an interpretable and usable output, if desired. We achieve competitive results on the IAM and ICFHR2016 READ data sets compared to the state-of-the-art without the use of a language model, and we significantly improve over any recent sequence-to-sequence approaches.
Author: Michael, Johannes and Labahn, Roger and Grüning, Tobias and Zöllner, Jochen
Booktitle: Proceedings of the 2019 15th International Conference on Document Analysis and Recognition
Series: ICDAR ’19
Pages: To appear
Note: Partially funded by the European Unions Horizon 2020 research and innovation programme under grant agreement No 674943 (READ) | inproceeding
Measuring the performance of text recognition and text line detection engines is an important step to objectively compare systems and their configuration. There exist well-established measures for both tasks separately. However, there is no sophisticated evaluation scheme to measure the quality of a combined text line detection and text recognition system. The F-measure on word level is a well-known methodology, which is sometimes used in this context. Nevertheless, it does not take into account the alignment of hypothesis and ground truth text and can lead to deceptive results. Since users of automatic information retrieval pipelines in the context of text recognition are mainly interested in the end-to-end performance of a given system, there is a strong need for such a measure. Hence, we present a measure to evaluate the quality of an end-to-end text recognition system. The basis for this measure is the well established and widely used character error rate, which is limited — in its original form — to aligned hypothesis and ground truth texts. The proposed measure is flexible in a way that it can be configured to penalize different reading orders between the hypothesis and ground truth and can take into account the geometric position of the text lines. Additionally, it can ignore over- and under- segmentation of text lines. With these parameters it is possible to get a measure fitting best to its own needs.
Author: Leifert, Gundram and Labahn, Roger and Grüning, Tobias and Leifert, Svenja
Booktitle: Proceedings of the 2019 15th International Conference on Document Analysis and Recognition
Series: ICDAR ’19
Pages: To appear
Note: Partially funded by the European Unions Horizon 2020 research and innovation programme under grant agreement No 674943 (READ) | inproceeding
We present a recognition and retrieval system for the ICDAR2017 Competition on Information Extraction in Historical Handwritten Records which successfully infers person names and other data from marriage records. The system extracts information from the line images with a high accuracy and outperforms the baseline. The optical model is based on Neural Networks. To infer the desired information, regular expressions are used to describe the set of feasible words sequences.
Author: Tobias Strauß and Max Weidemann and Johannes Michael and Gundram Leifert and Tobias Grüning and Roger Labahn
Journal: CoRR
Volume: abs/1804.09943
Note: Partially funded by the European Unions Horizon 2020 research and innovation programme under grant agreement No 674943 (READ)
Accessibility of the valuable cultural heritage which is hidden in countless scanned historical documents is the motivation for the presented dissertation. The developed (fully automatic) text line extraction methodology combines state-of-the-art machine learning techniques and modern image processing methods. It demonstrates its quality by outperforming several other approaches on a couple of benchmarking datasets. The method is already being used by a wide audience of researchers from different disciplines and thus contributes its (small) part to the aforementioned goal.
Author: Tobias Grüning
Type: PhD thesis
School: Universität Rostock
READ-BAD: A New Dataset and Evaluation Scheme for Baseline Detection in Archival Documents (04.2018)
Author: Tobias Grüning and Roger Labahn and Markus Diem and Florian Kleber and Stefan Fiel
Booktitle: 2018 13th IAPR International Workshop on Document Analysis Systems (DAS)
Pages: 351-356
Note: Partially funded by the European Unions Horizon 2020 research and innovation programme under grant agreement No 674943 (READ) | inproceeding
Doi: 10.1109/DAS.2018.38
Author: Grüning, Tobias and Leifert, Gundram and Strauß, Tobias and Labahn, Roger
Booktitle: 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR)
Volume: 01
Pages: 351-356
Note: Partially funded by the European Unions Horizon 2020 research and innovation programme under grant agreement No 674943 (READ) | inproceeding
Doi: 10.1109/ICDAR.2017.47
In der Handschrifterkennung geben neuronale Netze Folgen von Wahrscheinlichkeit pro Character aus. Gegenstand der Arbeit ist das Optimierungsproblem, die Ausgaben neuronaler Netzwerke in Maschinen-lesbare Texte zu konvertieren. Dies wird mit Hilfe von gewichteten Automaten realisiert. Als wesentliches Resultat wird eine effiziente Heuristik entwickelt, die die wahrscheinlichste Buchstabenfolge aller durch reguläre Ausdrücke beschränkter Folgen findet.
Author: Tobias Strauß
Type: PhD thesis
School: Universität Rostock
Author: Grüning, Tobias and Leifert, Gundram and Strauß, Tobias and Labahn, Roger
Booktitle: CLEF2016 Working Notes
Series: CEUR Workshop Proceedings
Publisher: CEUR-WS.org
Pages: 351-356
Note: Partially funded by grant no. KF2622304SS3 (Kooperationsprojekt) in Zentrales Innovationsprogramm Mittelstand (ZIM) by Bundesrepublik Deutschland (BMWi) and the European Unions Horizon 2020 research and innovation programme under grant agreement No 674943 (READ) | inproceeding
Doi: 10.1109/ICDAR.2017.47
The transcription of handwritten text on images is one task in machine learning and one solution to solve it is using multi- dimensional recurrent neural networks (MDRNN) with connectionist temporal classification (CTC). The RNNs can contain special units, the long short-term memory (LSTM) cells. They are able to learn long term dependencies but they get unstable when the dimension is chosen greater than one. We defined some useful and necessary properties for the one-dimensional LSTM cell and extend them in the multi-dimensional case. Thereby we introduce several new cells with better stability. We present a method to design cells using the theory of linear shift invariant systems. The new cells are compared to the LSTM cell on the IFN/ENIT and Rimes database, where we can improve the recognition rate compared to the LSTM cell. So each application where the LSTM cells in MDRNNs are used could be improved by substituting them by the new developed cells.
Author: Gundram Leifert and Tobias Strauß and Tobias Grüning and Welf Wustlich and Roger Labahn
Journal: Journal of Machine Learning Research
Volume: 17
Number: 97
Pages: 1-37
This article proposes a convenient tool for decoding the output of neural networks trained by Connectionist Temporal Classification (CTC) for handwritten text recognition. We use regular expressions to describe the complex structures expected in the writing. The corresponding finite automata are employed to build a decoder. We analyze theoretically which calculations are relevant and which can be avoided. A great speed-up results from an approximation. We conclude that the approximation most likely fails if the regular expression does not match the ground truth which is not harmful for many applications since the low probability will be even underestimated. The proposed decoder is very efficient compared to other decoding methods. The variety of applications reaches from information retrieval to full text recognition. We refer to applications where we integrated the proposed decoder successfully.
Author: Tobias Strauß and Gundram Leifert and Tobias Grüning and Roger Labahn
Journal: Neural Networks
Volume: 79
Pages: 1 – 11
Note: Partially funded by grant no. KF2622304SS3 (Kooperationsprojekt) in Zentrales Innovationsprogramm Mittelstand (ZIM) by Bundesrepublik Deutschland (BMWi)
We describe CITlab’s recognition system for the HTRtS competition attached to the 13. International Conference on Document Analysis and Recognition, ICDAR 2015. The task comprises the recognition of historical handwritten documents. The core algorithms of our system are based on multi-dimensional recurrent neural networks (MDRNN) and connectionist temporal classification (CTC). The software modules behind that as well as the basic utility technologies are essentially powered by PLANET’s ARGUS framework for intelligent text recognition and image processing.
Author: Gundram Leifert and Tobias Strauß and Tobias Grüning and Roger Labahn
Journal: CoRR
Volume: abs/1605.08412
Note: Partially funded by the European Unions Horizon 2020 research and innovation programme under grant agreement No 674943 (READ)
We describe CITlab’s recognition system for the HTRtS competition attached to the 14. International Conference on Frontiers in Handwriting Recognition, ICFHR 2014. The task comprises the recognition of historical handwritten documents. The core algorithms of our system are based on multi-dimensional recurrent neural networks (MDRNN) and connectionist temporal classification (CTC). The software modules behind that as well as the basic utility technologies are essentially powered by PLANET’s ARGUS framework for intelligent text recognition and image processing.
Author: Tobias Strauß and Tobias Grüning and Gundram Leifert and Roger Labahn
Journal: CoRR
Volume: abs/1412.3949
Note: Partially funded by research grant no. V220-630-08-TFMV-S/F-059 (Verbundvorhaben, Technologieförderung Land Mecklenburg-Vorpommern) in European Social / Regional Development Funds
We describe CITlab’s recognition system for the ANWRESH-2014 competition attached to the 14. International Conference on Frontiers in Handwriting Recognition, ICFHR 2014. The task comprises word recognition from segmented historical documents. The core components of our system are based on multi-dimensional recurrent neural networks (MDRNN) and connectionist temporal classification (CTC). The software modules behind that as well as the basic utility technologies are essentially powered by PLANET’s ARGUS framework for intelligent text recognition and image processing.
Author: Tobias Strauß and Tobias Grüning and Gundram Leifert and Roger Labahn
Journal: CoRR
Volume: abs/1412.6012
Note: Partially funded by research grant no. V220-630-08-TFMV-S/F-059 (Verbundvorhaben, Technologieförderung Land Mecklenburg-Vorpommern) in European Social / Regional Development Funds
In the recent years it turned out that multidimensional recurrent neural networks (MDRNN) perform very well for offline handwriting recognition tasks like the OpenHaRT 2013 evaluation DIR. With suitable writing preprocessing and dictionary lookup, our ARGUS software completed this task with an error rate of 26.27% in its primary setup.
Author: Tobias Strauß and Tobias Grüning and Gundram Leifert and Roger Labahn
Journal: CoRR
Volume: abs/1412.6061
Note: Partially funded by research grant no. V220-630-08-TFMV-S/F-059 (Verbundvorhaben, Technologieförderung Land Mecklenburg-Vorpommern) in European Social / Regional Development Funds
This article develops approaches to generate dynamical reservoirs of echo state networks with desired properties reducing the amount of randomness. It is possible to create weight matrices with a predefined singular value spectrum. The procedure guarantees stability (echo state property). We prove the minimization of the impact of noise on the training process. The resulting reservoir types are strongly related to reservoirs already known in the literature. Our experiments show that well-chosen input weights can improve performance.
Author: Strauss, Tobias and Wustlich, Welf and Labahn, Roger
Journal: Neural Computation
Volume: 24
Number: 12
Pages: 3246-3276
Note: Partially funded by the research grant no. V220-630-08-TFMV-S/F-059 (Verbundvorhaben, Technologieförderung Land Mecklenburg-Vorpommern) in European Social / Regional Development Funds
