A scalable pattern spotting system for historical documents

En, Sovann; Petitjean, Caroline; Nicolas, Stéphane; Heutte, Laurent

doi:10.1016/j.patcog.2016.01.014

Cited by 27 publications

(15 citation statements)

References 35 publications

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“…a Top category is one that is in the first 10 positions in the ranking ordered by mAP). We see that our proposal also has poor performance with the smallest queries, as the state-of-the-art system [2]. We think that the small size query problem could be tackled by considering a lower level of the pyramid, such as P 2 , to reduce the RF size and let the embedding focus mostly on the query and not also on the canvas.…”

Section: Image Retrieval and Pattern Spotting Resultsmentioning

confidence: 91%

“…2). Three important challenges come up with this particular dataset: (1) queries and documents are hand-drawn, introducing intraclass variation in the query-query and query-page relation, (2) queries are generally small (as small as ≈ 10 × 20 pixels) and (3) images are noisy and commonly degraded because all manuscripts are dated back from 10th to 16th century. 1447 possible queries among 35 different categories and 1500 pages are used for the experimentation.…”

Section: Experimental Protocolmentioning

confidence: 99%

“…For that they propose a benchmark image dataset of medieval images with groundtruth information and a detection system that accurately localizes objects. In [1,2], En et al have proposed a benchmark dataset, called "DocExplore", which is publicly available online [8], and a complete system able to perform image retrieval, using query by example paradigm, and pattern spotting tasks. By "pattern spotting" we mean the task of locating as precisely as possible, in the images of an indexed database of document images, the different instances of a given object, i.e.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Pattern Spotting in Historical Documents Using Convolutional Models

Úbeda

Saavedra²,

Nicolas

et al. 2019

Proceedings of the 5th International Workshop on Historical Document Imaging and Processing

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Pattern spotting consists of searching in a collection of historical document images for occurrences of a graphical object using an image query. Contrary to object detection, no prior information nor predefined class is given about the query so training a model of the object is not feasible. In this paper, a convolutional neural network approach is proposed to tackle this problem. We use RetinaNet as a feature extractor to obtain multiscale embeddings of the regions of the documents and also for the queries. Experiments conducted on the Doc-Explore dataset show that our proposal is better at locating patterns and requires less storage for indexing images than the state-of-the-art system, but fails at retrieving some pages containing multiple instances of the query.CCS Concepts • Computing methodologies → Visual content-based indexing and retrieval.

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Section: Image Retrieval and Pattern Spotting Resultsmentioning

confidence: 91%

Section: Experimental Protocolmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Pattern Spotting in Historical Documents Using Convolutional Models

Úbeda

Saavedra²,

Nicolas

et al. 2019

Proceedings of the 5th International Workshop on Historical Document Imaging and Processing

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“…No results have been reported on the first dataset so far. On the other hand, results of the authors of the second dataset showed clearly that there is room for improvement [21,22]. These two datasets are relevant to our own work and offer realistic scenarios in manuscript research, where very few labelled samples are available for each pattern to be detected (sometimes only one).…”

Section: Related Workmentioning

confidence: 85%

“…In contrast, several pattern-detection methods have been proposed to detect symbols, logos and other types of patterns found in documents [17][18][19][20]. Some of these methods have been dedicated to detecting patterns in historical documents and manuscripts [3,21,22]. This article is particularly concerned with detecting patterns in historical documents and aims to facilitate manuscript research.…”

Section: Related Workmentioning

confidence: 99%

Learning-free pattern detection for manuscript research:

Mohammed

Märgner

Ciotti

2021

IJDAR

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Automatic pattern detection has become increasingly important for scholars in the humanities as the number of manuscripts that have been digitised has grown. Most of the state-of-the-art methods used for pattern detection depend on the availability of a large number of training samples, which are typically not available in the humanities as they involve tedious manual annotation by researchers (e.g. marking the location and size of words, drawings, seals and so on). This makes the applicability of such methods very limited within the field of manuscript research. We propose a learning-free approach based on a state-of-the-art Naïve Bayes Nearest-Neighbour classifier for the task of pattern detection in manuscript images. The method has already been successfully applied to an actual research question from South Asian studies about palm-leaf manuscripts. Furthermore, state-of-the-art results have been achieved on two extremely challenging datasets, namely the AMADI_LontarSet dataset of handwriting on palm leaves for word-spotting and the DocExplore dataset of medieval manuscripts for pattern detection. A performance analysis is provided as well in order to facilitate later comparisons by other researchers. Finally, an easy-to-use implementation of the proposed method is developed as a software tool and made freely available.

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