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Author |
Mohamed Ali Souibgui; Alicia Fornes; Yousri Kessentini; Beata Megyesi |
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Title |
Few shots are all you need: A progressive learning approach for low resource handwritten text recognition |
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Journal Article |
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Year |
2022 |
Publication |
Pattern Recognition Letters |
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PRL |
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Volume |
160 |
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43-49 |
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Abstract |
Handwritten text recognition in low resource scenarios, such as manuscripts with rare alphabets, is a challenging problem. In this paper, we propose a few-shot learning-based handwriting recognition approach that significantly reduces the human annotation process, by requiring only a few images of each alphabet symbols. The method consists of detecting all the symbols of a given alphabet in a textline image and decoding the obtained similarity scores to the final sequence of transcribed symbols. Our model is first pretrained on synthetic line images generated from an alphabet, which could differ from the alphabet of the target domain. A second training step is then applied to reduce the gap between the source and the target data. Since this retraining would require annotation of thousands of handwritten symbols together with their bounding boxes, we propose to avoid such human effort through an unsupervised progressive learning approach that automatically assigns pseudo-labels to the unlabeled data. The evaluation on different datasets shows that our model can lead to competitive results with a significant reduction in human effort. The code will be publicly available in the following repository: https://github.com/dali92002/HTRbyMatching |
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Elsevier |
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DAG; 600.121; 600.162; 602.230 |
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no |
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Call Number |
Admin @ si @ SFK2022 |
Serial |
3736 |
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Author |
Pau Torras; Arnau Baro; Alicia Fornes; Lei Kang |
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Title |
Improving Handwritten Music Recognition through Language Model Integration |
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Conference Article |
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Year |
2022 |
Publication |
4th International Workshop on Reading Music Systems (WoRMS2022) |
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42-46 |
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Keywords |
optical music recognition; historical sources; diversity; music theory; digital humanities |
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Handwritten Music Recognition, especially in the historical domain, is an inherently challenging endeavour; paper degradation artefacts and the ambiguous nature of handwriting make recognising such scores an error-prone process, even for the current state-of-the-art Sequence to Sequence models. In this work we propose a way of reducing the production of statistically implausible output sequences by fusing a Language Model into a recognition Sequence to Sequence model. The idea is leveraging visually-conditioned and context-conditioned output distributions in order to automatically find and correct any mistakes that would otherwise break context significantly. We have found this approach to improve recognition results to 25.15 SER (%) from a previous best of 31.79 SER (%) in the literature. |
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November 18, 2022 |
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WoRMS |
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DAG; 600.121; 600.162; 602.230 |
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Call Number |
Admin @ si @ TBF2022 |
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3735 |
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Author |
Marçal Rusiñol; Josep Llados |
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Title |
A Region-Based Hashing Approach for Symbol Spotting in Thechnical Documents |
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Conference Article |
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Year |
2007 |
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Seventh IAPR International Workshop on Graphics Recognition |
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41–42 |
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Curitiba (Brazil) |
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J. Llados, W. Liu, J.M. Ogier |
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GREC |
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DAG |
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DAG @ dag @ RuL2007a |
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846 |
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Author |
Salim Jouili; Salvatore Tabbone; Ernest Valveny |
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Title |
Comparing Graph Similarity Measures for Graphical Recognition |
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Book Chapter |
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2010 |
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Graphics Recognition. Achievements, Challenges, and Evolution. 8th International Workshop, GREC 2009. Selected Papers |
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6020 |
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37-48 |
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In this paper we evaluate four graph distance measures. The analysis is performed for document retrieval tasks. For this aim, different kind of documents are used including line drawings (symbols), ancient documents (ornamental letters), shapes and trademark-logos. The experimental results show that the performance of each graph distance measure depends on the kind of data and the graph representation technique. |
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Springer Berlin Heidelberg |
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LNCS |
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0302-9743 |
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978-3-642-13727-3 |
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GREC |
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DAG |
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no |
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Admin @ si @ JTV2010 |
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2404 |
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Author |
Jaume Gibert; Ernest Valveny; Oriol Ramos Terrades; Horst Bunke |
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Title |
Multiple Classifiers for Graph of Words Embedding |
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Conference Article |
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2011 |
Publication |
10th International Conference on Multiple Classifier Systems |
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6713 |
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36-45 |
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During the last years, there has been an increasing interest in applying the multiple classifier framework to the domain of structural pattern recognition. Constructing base classifiers when the input patterns are graph based representations is not an easy problem. In this work, we make use of the graph embedding methodology in order to construct different feature vector representations for graphs. The graph of words embedding assigns a feature vector to every graph by counting unary and binary relations between node representatives and combining these pieces of information into a single vector. Selecting different node representatives leads to different vectorial representations and therefore to different base classifiers that can be combined. We experimentally show how this methodology significantly improves the classification of graphs with respect to single base classifiers. |
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Napoles, Italy |
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Carlo Sansone; Josef Kittler; Fabio Roli |
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LNCS |
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978-3-642-21556-8 |
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MCS |
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DAG |
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no |
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Admin @ si @GVR2011 |
Serial |
1745 |
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Author |
David Fernandez; Simone Marinai; Josep Llados; Alicia Fornes |
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Title |
Contextual Word Spotting in Historical Manuscripts using Markov Logic Networks |
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Conference Article |
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2013 |
Publication |
2nd International Workshop on Historical Document Imaging and Processing |
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36-43 |
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Natural languages can often be modelled by suitable grammars whose knowledge can improve the word spotting results. The implicit contextual information is even more useful when dealing with information that is intrinsically described as one collection of records. In this paper, we present one approach to word spotting which uses the contextual information of records to improve the results. The method relies on Markov Logic Networks to probabilistically model the relational organization of handwritten records. The performance has been evaluated on the Barcelona Marriages Dataset that contains structured handwritten records that summarize marriage information. |
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washington; USA; August 2013 |
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978-1-4503-2115-0 |
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HIP |
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DAG; 600.056; 600.045; 600.061; 602.006 |
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no |
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Call Number |
Admin @ si @ FML2013 |
Serial |
2308 |
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Author |
Thanh Ha Do; Salvatore Tabbone; Oriol Ramos Terrades |
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Title |
Sparse representation over learned dictionary for symbol recognition |
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Journal Article |
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Year |
2016 |
Publication |
Signal Processing |
Abbreviated Journal |
SP |
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125 |
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36-47 |
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Keywords |
Symbol Recognition; Sparse Representation; Learned Dictionary; Shape Context; Interest Points |
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In this paper we propose an original sparse vector model for symbol retrieval task. More speci cally, we apply the K-SVD algorithm for learning a visual dictionary based on symbol descriptors locally computed around interest points. Results on benchmark datasets show that the obtained sparse representation is competitive related to state-of-the-art methods. Moreover, our sparse representation is invariant to rotation and scale transforms and also robust to degraded images and distorted symbols. Thereby, the learned visual dictionary is able to represent instances of unseen classes of symbols. |
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DAG; 600.061; 600.077 |
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Admin @ si @ DTR2016 |
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2946 |
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Author |
Jialuo Chen; Mohamed Ali Souibgui; Alicia Fornes; Beata Megyesi |
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Title |
Unsupervised Alphabet Matching in Historical Encrypted Manuscript Images |
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Conference Article |
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Year |
2021 |
Publication |
4th International Conference on Historical Cryptology |
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34-37 |
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Abstract |
Historical ciphers contain a wide range ofsymbols from various symbol sets. Iden-tifying the cipher alphabet is a prerequi-site before decryption can take place andis a time-consuming process. In this workwe explore the use of image processing foridentifying the underlying alphabet in ci-pher images, and to compare alphabets be-tween ciphers. The experiments show thatciphers with similar alphabets can be suc-cessfully discovered through clustering. |
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Virtual; September 2021 |
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HistoCrypt |
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DAG; 602.230; 600.140; 600.121 |
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Admin @ si @ CSF2021 |
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3617 |
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Author |
Albert Gordo; Florent Perronnin; Yunchao Gong; Svetlana Lazebnik |
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Title |
Asymmetric Distances for Binary Embeddings |
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Journal Article |
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2014 |
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IEEE Transactions on Pattern Analysis and Machine Intelligence |
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TPAMI |
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36 |
Issue |
1 |
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33-47 |
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In large-scale query-by-example retrieval, embedding image signatures in a binary space offers two benefits: data compression and search efficiency. While most embedding algorithms binarize both query and database signatures, it has been noted that this is not strictly a requirement. Indeed, asymmetric schemes which binarize the database signatures but not the query still enjoy the same two benefits but may provide superior accuracy. In this work, we propose two general asymmetric distances which are applicable to a wide variety of embedding techniques including Locality Sensitive Hashing (LSH), Locality Sensitive Binary Codes (LSBC), Spectral Hashing (SH), PCA Embedding (PCAE), PCA Embedding with random rotations (PCAE-RR), and PCA Embedding with iterative quantization (PCAE-ITQ). We experiment on four public benchmarks containing up to 1M images and show that the proposed asymmetric distances consistently lead to large improvements over the symmetric Hamming distance for all binary embedding techniques. |
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0162-8828 |
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DAG; 600.045; 605.203; 600.077 |
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Admin @ si @ GPG2014 |
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2272 |
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Author |
Agnes Borras; Josep Llados |
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Title |
Similarity-Based Object Retrieval Using Appearance and Geometric Feature Combination |
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Book Chapter |
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Year |
2007 |
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3rd Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA 2007), J. Marti et al. (Eds.) LNCS 4477:113–120 |
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LNCS |
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4478 |
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33–39 |
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This work presents a content-based image retrieval system of general purpose that deals with cluttered scenes containing a given query object. The system is flexible enough to handle with a single image of an object despite its rotation, translation and scale variations. The image content is divided in parts that are described with a combination of features based on geometrical and color properties. The idea behind the feature combination is to benefit from a fuzzy similarity computation that provides robustness and tolerance to the retrieval process. The features can be independently computed and the image parts can be easily indexed by using a table structure on every feature value. Finally a process inspired in the alignment strategies is used to check the coherence of the object parts found in a scene. Our work presents a system of easy implementation that uses an open set of features and can suit a wide variety of applications. |
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Girona (Spain) |
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978-3-540-72848-1 |
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DAG; |
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DAG @ dag @ BoL2007a; IAM @ iam @ BoL2007a |
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776 |
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