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Author |
Marçal Rusiñol; Lluis Pere de las Heras; Oriol Ramos Terrades |
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Title |
Flowchart Recognition for Non-Textual Information Retrieval in Patent Search |
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Journal Article |
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Year |
2014 |
Publication |
Information Retrieval |
Abbreviated Journal |
IR |
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17 |
Issue |
5-6 |
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545-562 |
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Flowchart recognition; Patent documents; Text/graphics separation; Raster-to-vector conversion; Symbol recognition |
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Abstract |
Relatively little research has been done on the topic of patent image retrieval and in general in most of the approaches the retrieval is performed in terms of a similarity measure between the query image and the images in the corpus. However, systems aimed at overcoming the semantic gap between the visual description of patent images and their conveyed concepts would be very helpful for patent professionals. In this paper we present a flowchart recognition method aimed at achieving a structured representation of flowchart images that can be further queried semantically. The proposed method was submitted to the CLEF-IP 2012 flowchart recognition task. We report the obtained results on this dataset. |
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1386-4564 |
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DAG; 600.077 |
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Admin @ si @ RHR2013 |
Serial |
2342 |
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Author |
Albert Gordo; Florent Perronnin; Ernest Valveny |
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Title |
Large-scale document image retrieval and classification with runlength histograms and binary embeddings |
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Journal Article |
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Year |
2013 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
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46 |
Issue |
7 |
Pages |
1898-1905 |
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Keywords |
visual document descriptor; compression; large-scale; retrieval; classification |
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We present a new document image descriptor based on multi-scale runlength
histograms. This descriptor does not rely on layout analysis and can be
computed efficiently. We show how this descriptor can achieve state-of-theart
results on two very different public datasets in classification and retrieval
tasks. Moreover, we show how we can compress and binarize these descriptors
to make them suitable for large-scale applications. We can achieve state-ofthe-
art results in classification using binary descriptors of as few as 16 to 64
bits. |
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Elsevier |
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0031-3203 |
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DAG; 600.042; 600.045; 605.203 |
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Admin @ si @ GPV2013 |
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2306 |
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Author |
Albert Gordo; Alicia Fornes; Ernest Valveny |
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Title |
Writer identification in handwritten musical scores with bags of notes |
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Journal Article |
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Year |
2013 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
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46 |
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5 |
Pages |
1337-1345 |
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Writer Identification is an important task for the automatic processing of documents. However, the identification of the writer in graphical documents is still challenging. In this work, we adapt the Bag of Visual Words framework to the task of writer identification in handwritten musical scores. A vanilla implementation of this method already performs comparably to the state-of-the-art. Furthermore, we analyze the effect of two improvements of the representation: a Bhattacharyya embedding, which improves the results at virtually no extra cost, and a Fisher Vector representation that very significantly improves the results at the cost of a more complex and costly representation. Experimental evaluation shows results more than 20 points above the state-of-the-art in a new, challenging dataset. |
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0031-3203 |
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DAG |
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Admin @ si @ GFV2013 |
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2307 |
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Author |
Volkmar Frinken; Andreas Fischer; Markus Baumgartner; Horst Bunke |
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Title |
Keyword spotting for self-training of BLSTM NN based handwriting recognition systems |
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Journal Article |
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Year |
2014 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
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47 |
Issue |
3 |
Pages |
1073-1082 |
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Keywords |
Document retrieval; Keyword spotting; Handwriting recognition; Neural networks; Semi-supervised learning |
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Abstract |
The automatic transcription of unconstrained continuous handwritten text requires well trained recognition systems. The semi-supervised paradigm introduces the concept of not only using labeled data but also unlabeled data in the learning process. Unlabeled data can be gathered at little or not cost. Hence it has the potential to reduce the need for labeling training data, a tedious and costly process. Given a weak initial recognizer trained on labeled data, self-training can be used to recognize unlabeled data and add words that were recognized with high confidence to the training set for re-training. This process is not trivial and requires great care as far as selecting the elements that are to be added to the training set is concerned. In this paper, we propose to use a bidirectional long short-term memory neural network handwritten recognition system for keyword spotting in order to select new elements. A set of experiments shows the high potential of self-training for bootstrapping handwriting recognition systems, both for modern and historical handwritings, and demonstrate the benefits of using keyword spotting over previously published self-training schemes. |
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DAG; 600.077; 602.101 |
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Admin @ si @ FFB2014 |
Serial |
2297 |
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Author |
Veronica Romero; Alicia Fornes; Nicolas Serrano; Joan Andreu Sanchez; A.H. Toselli; Volkmar Frinken; E. Vidal; Josep Llados |
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Title |
The ESPOSALLES database: An ancient marriage license corpus for off-line handwriting recognition |
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Journal Article |
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Year |
2013 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
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Volume |
46 |
Issue |
6 |
Pages |
1658-1669 |
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Abstract |
Historical records of daily activities provide intriguing insights into the life of our ancestors, useful for demography studies and genealogical research. Automatic processing of historical documents, however, has mostly been focused on single works of literature and less on social records, which tend to have a distinct layout, structure, and vocabulary. Such information is usually collected by expert demographers that devote a lot of time to manually transcribe them. This paper presents a new database, compiled from a marriage license books collection, to support research in automatic handwriting recognition for historical documents containing social records. Marriage license books are documents that were used for centuries by ecclesiastical institutions to register marriage licenses. Books from this collection are handwritten and span nearly half a millennium until the beginning of the 20th century. In addition, a study is presented about the capability of state-of-the-art handwritten text recognition systems, when applied to the presented database. Baseline results are reported for reference in future studies. |
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Elsevier Science Inc. New York, NY, USA |
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0031-3203 |
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DAG; 600.045; 602.006; 605.203 |
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Admin @ si @ RFS2013 |
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2298 |
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