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- Hast, Anders, et al.
(författare)
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An intelligent user interface for efficient semi-automatic transcription of historical handwritten documents
- 2018
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Ingår i: Proc. 23rd International Conference on Intelligent User Interfaces Companion. - New York : ACM Press. - 9781450355711
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Konferensbidrag (refereegranskat)abstract
- Transcription of large-scale historical handwritten document images is a tedious task. Machine learning techniques, such as deep learning, are popularly used for quick transcription, but often require a substantial amount of pre-transcribed word examples for training. Instead of line-by-line word transcription, this paper proposes a simple training-free gamification strategy where all occurrences of each arbitrarily selected word is transcribed once, using an intelligent user interface implemented in this work. The proposed approach offers a fast and user-friendly semi-automatic transcription that allows multiple users to work on the same document collection simultaneously.
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- Hast, Anders, et al.
(författare)
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Embedded Prototype Subspace Classification : A subspace learning framework
- 2019
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Ingår i: Computer Analysis of Images and Patterns, CAIP 2019, PT II. - Cham : Springer. - 9783030298913 - 9783030298906 ; , s. 581-592
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Konferensbidrag (refereegranskat)abstract
- Handwritten text recognition is a daunting task, due to complex characteristics of handwritten letters. Deep learning based methods have achieved significant advances in recognizing challenging handwritten texts because of its ability to learn and accurately classify intricate patterns. However, there are some limitations of deep learning, such as lack of well-defined mathematical model, black-box learning mechanism, etc., which pose challenges. This paper aims at going beyond the blackbox learning and proposes a novel learning framework called as Embedded Prototype Subspace Classification, that is based on the well-known subspace method, to recognise handwritten letters in a fast and efficient manner. The effectiveness of the proposed framework is empirically evaluated on popular datasets using standard evaluation measures.
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- Hast, Anders, et al.
(författare)
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Radial line Fourier descriptor for historical handwritten text representation
- 2018
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Ingår i: Proc. 26th International Conference on Computer Graphics.
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Automatic recognition of historical handwritten manuscripts is a daunting task due to paper degradation over time. Recognition-free retrieval or word spotting is popularly used for information retrieval and digitization of the historical handwritten documents. However, the performance of word spotting algorithms depends heavily on feature detection and representation methods. Although there exist popular feature descriptors such as Scale Invariant Feature Transform (SIFT) and Speeded Up Robust Features (SURF), the invariant properties of these descriptors amplify the noise in the degraded document images, rendering them more sensitive to noise and complex characteristics of historical manuscripts. Therefore, an efficient and relaxed feature descriptor is required as handwritten words across different documents are indeed similar, but not identical. This paper introduces a Radial Line Fourier (RLF) descriptor for handwritten word representation, with a short feature vector of 32 dimensions. A segmentation-free and training-free handwritten word spotting method is studied herein that relies on the proposed RLF descriptor, takes into account different keypoint representations and uses a simple preconditioner-based feature matching algorithm. The effectiveness of the RLF descriptor for segmentation-free handwritten word spotting is empirically evaluated on well-known historical handwritten datasets using standard evaluation measures.
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| 8. |
- Hast, Anders, et al.
(författare)
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Subspace Learning and Classification
- 2019
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Ingår i: Proc. 3rd Swedish Symposium on Deep Learning.
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)
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| 9. |
- Hast, Anders, et al.
(författare)
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TexT – Text extractor tool for handwritten document transcription and annotation
- 2018
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Ingår i: Digital Libraries and Multimedia Archives. - Cham : Springer. - 9783319731643 - 9783319731650 ; , s. 81-92
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Konferensbidrag (refereegranskat)abstract
- This paper presents a framework for semi-automatic transcription of large-scale historical handwritten documents and proposes a simple user-friendly text extractor tool, TexT for transcription. The proposed approach provides a quick and easy transcription of text using computer assisted interactive technique. The algorithm finds multiple occurrences of the marked text on-the-fly using a word spotting system. TexT is also capable of performing on-the-fly annotation of handwritten text with automatic generation of ground truth labels, and dynamic adjustment and correction of user generated bounding box annotations with the word being perfectly encapsulated. The user can view the document and the found words in the original form or with background noise removed for easier visualization of transcription results. The effectiveness of TexT is demonstrated on an archival manuscript collection from well-known publicly available dataset.
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| 10. |
- Hast, Anders, 1966-, et al.
(författare)
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Word Recognition using Embedded Prototype Subspace Classifiers on a new Imbalanced Dataset
- 2021
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Ingår i: Journal of WSCG. - : University of West Bohemia. - 1213-6972 .- 1213-6964. ; 29:1-2, s. 39-47
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents an approach towards word recognition based on embedded prototype subspace classification. The purpose of this paper is three-fold. Firstly, a new dataset for word recognition is presented, which is extracted from the Esposalles database consisting of the Barcelona cathedral marriage records. Secondly, different clustering techniques are evaluated for Embedded Prototype Subspace Classifiers. The dataset, containing 30 different classes of words is heavily imbalanced, and some word classes are very similar, which renders the classification task rather challenging. For ease of use, no stratified sampling is done in advance, and the impact of different data splits is evaluated for different clustering techniques. It will be demonstrated that the original clustering technique based on scaling the bandwidth has to be adjusted for this new dataset. Thirdly, an algorithm is therefore proposed that finds k clusters, striving to obtain a certain amount of feature points in each cluster, rather than finding some clusters based on scaling the Silverman’s rule of thumb. Furthermore, Self Organising Maps are also evaluated as both a clustering and embedding technique.
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