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Author |
S. Chanda; Umapada Pal; Oriol Ramos Terrades |
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Title |
Word-Wise Thai and Roman Script Identification |
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Journal |
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2009 |
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ACM Transactions on Asian Language Information Processing |
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TALIP |
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8 |
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3 |
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1-21 |
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Abstract |
In some Thai documents, a single text line of a printed document page may contain words of both Thai and Roman scripts. For the Optical Character Recognition (OCR) of such a document page it is better to identify, at first, Thai and Roman script portions and then to use individual OCR systems of the respective scripts on these identified portions. In this article, an SVM-based method is proposed for identification of word-wise printed Roman and Thai scripts from a single line of a document page. Here, at first, the document is segmented into lines and then lines are segmented into character groups (words). In the proposed scheme, we identify the script of a character group combining different character features obtained from structural shape, profile behavior, component overlapping information, topological properties, and water reservoir concept, etc. Based on the experiment on 10,000 data (words) we obtained 99.62% script identification accuracy from the proposed scheme. |
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1530-0226 |
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DAG |
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no |
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Admin @ si @ CPR2009f |
Serial |
1869 |
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Author |
Mohamed Ali Souibgui; Asma Bensalah; Jialuo Chen; Alicia Fornes; Michelle Waldispühl |
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Title |
A User Perspective on HTR methods for the Automatic Transcription of Rare Scripts: The Case of Codex Runicus Just Accepted |
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Journal Article |
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Year |
2023 |
Publication |
ACM Journal on Computing and Cultural Heritage |
Abbreviated Journal |
JOCCH |
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15 |
Issue |
4 |
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1-18 |
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Recent breakthroughs in Artificial Intelligence, Deep Learning and Document Image Analysis and Recognition have significantly eased the creation of digital libraries and the transcription of historical documents. However, for documents in rare scripts with few labelled training data available, current Handwritten Text Recognition (HTR) systems are too constraint. Moreover, research on HTR often focuses on technical aspects only, and rarely puts emphasis on implementing software tools for scholars in Humanities. In this article, we describe, compare and analyse different transcription methods for rare scripts. We evaluate their performance in a real use case of a medieval manuscript written in the runic script (Codex Runicus) and discuss advantages and disadvantages of each method from the user perspective. From this exhaustive analysis and comparison with a fully manual transcription, we raise conclusions and provide recommendations to scholars interested in using automatic transcription tools. |
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ACM |
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DAG; 600.121; 600.162; 602.230; 600.140 |
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Admin @ si @ SBC2023 |
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3732 |
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Author |
Y. Patel; Lluis Gomez; Marçal Rusiñol; Dimosthenis Karatzas; C.V. Jawahar |
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Title |
Self-Supervised Visual Representations for Cross-Modal Retrieval |
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Conference Article |
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2019 |
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ACM International Conference on Multimedia Retrieval |
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182–186 |
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Cross-modal retrieval methods have been significantly improved in last years with the use of deep neural networks and large-scale annotated datasets such as ImageNet and Places. However, collecting and annotating such datasets requires a tremendous amount of human effort and, besides, their annotations are limited to discrete sets of popular visual classes that may not be representative of the richer semantics found on large-scale cross-modal retrieval datasets. In this paper, we present a self-supervised cross-modal retrieval framework that leverages as training data the correlations between images and text on the entire set of Wikipedia articles. Our method consists in training a CNN to predict: (1) the semantic context of the article in which an image is more probable to appear as an illustration, and (2) the semantic context of its caption. Our experiments demonstrate that the proposed method is not only capable of learning discriminative visual representations for solving vision tasks like classification, but that the learned representations are better for cross-modal retrieval when compared to supervised pre-training of the network on the ImageNet dataset. |
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Otawa; Canada; june 2019 |
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ICMR |
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DAG; 600.121; 600.129 |
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Admin @ si @ PGR2019 |
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3288 |
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Author |
Lluis Pere de las Heras; Joan Mas; Gemma Sanchez; Ernest Valveny |
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Title |
Descriptor-based Svm Wall Detector |
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Conference Article |
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Year |
2011 |
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9th International Workshop on Graphic Recognition |
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Architectural floorplans exhibit a large variability in notation. Therefore, segmenting and identifying the elements of any kind of plan becomes a challenging task for approaches based on grouping structural primitives obtained by vectorization. Recently, a patch-based segmentation method working at pixel level and relying on the construction of a visual vocabulary has been proposed showing its adaptability to different notations by automatically learning the visual appearance of the elements in each different notation. In this paper we describe an evolution of this new approach in two directions: firstly we evaluate different features to obtain the description of every patch. Secondly, we train an SVM classifier to obtain the category of every patch instead of constructing a visual vocabulary. These modifications of the method have been tested for wall detection on two datasets of architectural floorplans with different notations and compared with the results obtained with the original approach. |
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GREC |
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no |
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Admin @ si @ HMS2011b |
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1819 |
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Author |
Jose Antonio Rodriguez; Gemma Sanchez; Josep Llados |
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Title |
A Pen-based Interface for Real-time Document Edition |
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Conference Article |
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2007 |
Publication |
9th International Conference on Document Analysis and Recognition. |
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2 |
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939–944 |
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Curitiba (Brazil) |
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ICDAR |
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DAG @ dag @ RSL2007b |
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883 |
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Author |
Miquel Ferrer; Ernest Valveny |
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Title |
Combination of OCR Engines for Page Segmentation based on Performance Evaluation |
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Conference Article |
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2007 |
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9th International Conference on Document Analysis and Recognition |
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2 |
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784–788 |
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Curitiba (Brazil) |
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DAG @ dag @ FeV2007 |
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838 |
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Author |
Josep Llados; Gemma Sanchez |
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Title |
Indexing Historical Documents by Word Shape Signatures |
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Conference Article |
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2007 |
Publication |
9th International Conference on Document Analysis and Recognition |
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1 |
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362–366 |
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Curitiba (Brasil) |
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DAG @ dag @ LlS2007 |
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882 |
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Author |
Oriol Ramos Terrades; Salvatore Tabbone; Ernest Valveny |
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Title |
A Review of Shape Descriptors for Document Analysis |
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Conference Article |
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2007 |
Publication |
9th International Conference on Document Analysis and Recognition |
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1 |
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227–231 |
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Curitiba (Brazil) |
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DAG @ dag @ RTV2007b |
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884 |
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Author |
S. Chanda; Oriol Ramos Terrades; Umapada Pal |
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Title |
SVM Based Scheme for Thai and English Script Identification |
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Conference Article |
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2007 |
Publication |
9th International Conference on Document Analysis and Recognition |
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1 |
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551–555 |
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Curitiba (Brazil) |
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DAG @ dag @ CRP2007a |
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885 |
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Author |
Joan Mas; Gemma Sanchez; Josep Llados; B. Lamiroy |
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Title |
An Incremental On-line Parsing Algorithm for Recognizing Sketching Diagrams |
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Conference Article |
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2007 |
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9th IEEE International Conference on Document Analysis and Recognition |
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1 |
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452–456 |
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Curitiba (Brazil) |
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DAG @ dag @ MSL2007a |
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847 |
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