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Author |
Ali Furkan Biten; Ruben Tito; Lluis Gomez; Ernest Valveny; Dimosthenis Karatzas |
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Title |
OCR-IDL: OCR Annotations for Industry Document Library Dataset |
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Conference Article |
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2022 |
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ECCV Workshop on Text in Everything |
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Pretraining has proven successful in Document Intelligence tasks where deluge of documents are used to pretrain the models only later to be finetuned on downstream tasks. One of the problems of the pretraining approaches is the inconsistent usage of pretraining data with different OCR engines leading to incomparable results between models. In other words, it is not obvious whether the performance gain is coming from diverse usage of amount of data and distinct OCR engines or from the proposed models. To remedy the problem, we make public the OCR annotations for IDL documents using commercial OCR engine given their superior performance over open source OCR models. The contributed dataset (OCR-IDL) has an estimated monetary value over 20K US$. It is our hope that OCR-IDL can be a starting point for future works on Document Intelligence. All of our data and its collection process with the annotations can be found in this https URL. |
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ECCV |
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DAG; no proj |
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Admin @ si @ BTG2022 |
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3817 |
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Author |
Marçal Rusiñol; David Aldavert; Dimosthenis Karatzas; Ricardo Toledo; Josep Llados |
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Title |
Interactive Trademark Image Retrieval by Fusing Semantic and Visual Content. Advances in Information Retrieval |
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Conference Article |
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2011 |
Publication |
33rd European Conference on Information Retrieval |
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6611 |
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314-325 |
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In this paper we propose an efficient queried-by-example retrieval system which is able to retrieve trademark images by similarity from patent and trademark offices' digital libraries. Logo images are described by both their semantic content, by means of the Vienna codes, and their visual contents, by using shape and color as visual cues. The trademark descriptors are then indexed by a locality-sensitive hashing data structure aiming to perform approximate k-NN search in high dimensional spaces in sub-linear time. The resulting ranked lists are combined by using the Condorcet method and a relevance feedback step helps to iteratively revise the query and refine the obtained results. The experiments demonstrate the effectiveness and efficiency of this system on a realistic and large dataset. |
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Dublin, Ireland |
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Springer |
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Berlin |
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P. Clough; C. Foley; C. Gurrin; G.J.F. Jones; W. Kraaij; H. Lee; V. Murdoch |
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978-3-642-20160-8 |
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ECIR |
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DAG; RV;ADAS |
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Admin @ si @ RAK2011 |
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1737 |
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Adria Rico; Alicia Fornes |
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Title |
Camera-based Optical Music Recognition using a Convolutional Neural Network |
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2017 |
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12th IAPR International Workshop on Graphics Recognition |
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27-28 |
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optical music recognition; document analysis; convolutional neural network; deep learning |
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Optical Music Recognition (OMR) consists in recognizing images of music scores. Contrary to expectation, the current OMR systems usually fail when recognizing images of scores captured by digital cameras and smartphones. In this work, we propose a camera-based OMR system based on Convolutional Neural Networks, showing promising preliminary results |
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GREC |
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DAG;600.097; 600.121 |
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Admin @ si @ RiF2017 |
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3059 |
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Author |
Ernest Valveny; Antonio Lopez |
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Title |
Numeral Recognition for Quality Control of Surgical Sachets |
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Miscellaneous |
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2003 |
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Proceedings of the Seventh International Conference on Document Analysis and Recognition (ICDAR´03), 379–383 |
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DAG;ADAS |
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ADAS @ adas @ VaL2003 |
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423 |
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Author |
Marçal Rusiñol; David Aldavert; Ricardo Toledo; Josep Llados |
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Title |
Browsing Heterogeneous Document Collections by a Segmentation-Free Word Spotting Method |
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Conference Article |
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2011 |
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11th International Conference on Document Analysis and Recognition |
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63-67 |
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In this paper, we present a segmentation-free word spotting method that is able to deal with heterogeneous document image collections. We propose a patch-based framework where patches are represented by a bag-of-visual-words model powered by SIFT descriptors. A later refinement of the feature vectors is performed by applying the latent semantic indexing technique. The proposed method performs well on both handwritten and typewritten historical document images. We have also tested our method on documents written in non-Latin scripts. |
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Beijing, China |
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ICDAR |
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DAG;ADAS |
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no |
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Admin @ si @ RAT2011 |
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1788 |
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Author |
Robert Benavente; Gemma Sanchez; Ramon Baldrich; Maria Vanrell; Josep Llados |
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Normalized colour segmentation for human appearance description. |
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2000 |
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15 th International Conference on Pattern Recognition |
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3 |
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637-641 |
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Barcelona. |
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ICPR |
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DAG;CIC |
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CAT @ cat @ BSB2000 |
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223 |
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Author |
Francesc Tous; Agnes Borras; Robert Benavente; Ramon Baldrich; Maria Vanrell; Josep Llados |
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Title |
Textual Descriptors for browsing people by visual appearence. |
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Conference Article |
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2002 |
Publication |
5è. Congrés Català d’Intel·ligència Artificial CCIA |
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Image retrieval, textual descriptors, colour naming, colour normalization, graph matching. |
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This paper presents a first approach to build colour and structural descriptors for information retrieval on a people database. Queries are formulated in terms of their appearance that allows to seek people wearing specific clothes of a given colour name or texture. Descriptors are automatically computed by following three essential steps. A colour naming labelling from pixel properties. A region seg- mentation step based on colour properties of pixels combined with edge information. And a high level step that models the region arrangements in order to build clothes structure. Results are tested on large set of images from real scenes taken at the entrance desk of a building. |
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DAG;CIC |
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CAT @ cat @ TBB2002a |
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287 |
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Author |
Francesc Tous; Agnes Borras; Robert Benavente; Ramon Baldrich; Maria Vanrell; Josep Llados |
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Title |
Textual Descriptions for Browsing People by Visual Apperance. |
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2002 |
Publication |
Lecture Notes in Artificial Intelligence |
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2504 |
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419-429 |
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Abstract |
This paper presents a first approach to build colour and structural descriptors for information retrieval on a people database. Queries are formulated in terms of their appearance that allows to seek people wearing specific clothes of a given colour name or texture. Descriptors are automatically computed by following three essential steps. A colour naming labelling from pixel properties. A region seg- mentation step based on colour properties of pixels combined with edge information. And a high level step that models the region arrangements in order to build clothes structure. Results are tested on large set of images from real scenes taken at the entrance desk of a building |
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Springer Verlag |
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DAG;CIC |
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CAT @ cat @ TBB2002b |
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319 |
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Author |
Agnes Borras; Francesc Tous; Josep Llados; Maria Vanrell |
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Title |
High-Level Clothes Description Based on Colour-Texture and Structural Features |
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Conference Article |
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2003 |
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1rst. Iberian Conference on Pattern Recognition and Image Analysis IbPRIA 2003 |
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2652 |
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108-116 |
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Abstract |
ecture Notes in Computer Science 2652 108–116 |
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Palma de Mallorca |
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IbPRIA |
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DAG;CIC |
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CAT @ cat @ BTL2003b |
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369 |
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Author |
Partha Pratim Roy; Eduard Vazquez; Josep Llados; Ramon Baldrich; Umapada Pal |
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Title |
A System to Segment Text and Symbols from Color Maps |
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2008 |
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Graphics Recognition. Recent Advances and New Opportunities |
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5046 |
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245-256 |
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DAG;CIC |
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CAT @ cat @ RVL2008 |
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1005 |
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