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Sangheeta Roy; Palaiahnakote Shivakumara; Namita Jain; Vijeta Khare; Anjan Dutta; Umapada Pal; Tong Lu |
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Title |
Rough-Fuzzy based Scene Categorization for Text Detection and Recognition in Video |
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Journal Article |
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Year |
2018 |
Publication |
Pattern Recognition |
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PR |
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80 |
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64-82 |
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Rough set; Fuzzy set; Video categorization; Scene image classification; Video text detection; Video text recognition |
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Scene image or video understanding is a challenging task especially when number of video types increases drastically with high variations in background and foreground. This paper proposes a new method for categorizing scene videos into different classes, namely, Animation, Outlet, Sports, e-Learning, Medical, Weather, Defense, Economics, Animal Planet and Technology, for the performance improvement of text detection and recognition, which is an effective approach for scene image or video understanding. For this purpose, at first, we present a new combination of rough and fuzzy concept to study irregular shapes of edge components in input scene videos, which helps to classify edge components into several groups. Next, the proposed method explores gradient direction information of each pixel in each edge component group to extract stroke based features by dividing each group into several intra and inter planes. We further extract correlation and covariance features to encode semantic features located inside planes or between planes. Features of intra and inter planes of groups are then concatenated to get a feature matrix. Finally, the feature matrix is verified with temporal frames and fed to a neural network for categorization. Experimental results show that the proposed method outperforms the existing state-of-the-art methods, at the same time, the performances of text detection and recognition methods are also improved significantly due to categorization. |
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DAG; 600.097; 600.121 |
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Admin @ si @ RSJ2018 |
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3096 |
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Marçal Rusiñol; Lluis Gomez |
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Avances en clasificación de imágenes en los últimos diez años. Perspectivas y limitaciones en el ámbito de archivos fotográficos históricos |
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2018 |
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Revista anual de la Asociación de Archiveros de Castilla y León |
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21 |
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161-174 |
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DAG; 600.121; 600.129 |
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Admin @ si @ RuG2018 |
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3239 |
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Marçal Rusiñol; Josep Llados |
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Title |
The Role of the Users in Handwritten Word Spotting Applications: Query Fusion and Relevance Feedback |
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2012 |
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13th International Conference on Frontiers in Handwriting Recognition |
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55-60 |
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In this paper we present the importance of including the user in the loop in a handwritten word spotting framework. Several off-the-shelf query fusion and relevance feedback strategies have been tested in the handwritten word spotting context. The increase in terms of precision when the user is included in the loop is assessed using two datasets of historical handwritten documents and a baseline word spotting approach based on a bag-of-visual-words model. |
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Bari, Italy |
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978-1-4673-2262-1 |
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ICFHR |
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DAG |
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Admin @ si @ RuL2012 |
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2054 |
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Marçal Rusiñol; Josep Llados |
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Title |
Boosting the Handwritten Word Spotting Experience by Including the User in the Loop |
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2014 |
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Pattern Recognition |
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PR |
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47 |
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3 |
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1063–1072 |
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Handwritten word spotting; Query by example; Relevance feedback; Query fusion; Multidimensional scaling |
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In this paper, we study the effect of taking the user into account in a query-by-example handwritten word spotting framework. Several off-the-shelf query fusion and relevance feedback strategies have been tested in the handwritten word spotting context. The increase in terms of precision when the user is included in the loop is assessed using two datasets of historical handwritten documents and two baseline word spotting approaches both based on the bag-of-visual-words model. We finally present two alternative ways of presenting the results to the user that might be more attractive and suitable to the user's needs than the classic ranked list. |
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0031-3203 |
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DAG; 600.045; 600.061; 600.077 |
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Admin @ si @ RuL2013 |
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2343 |
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Marçal Rusiñol; Josep Llados |
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Title |
Flowchart Recognition in Patent Information Retrieval |
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2017 |
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Current Challenges in Patent Information Retrieval |
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37 |
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351-368 |
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Springer Berlin Heidelberg |
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M. Lupu; K. Mayer; N. Kando; A.J. Trippe |
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DAG; 600.097; 600.121 |
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Admin @ si @ RuL2017 |
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2896 |
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Author |
Marçal Rusiñol |
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Title |
Classificació semàntica i visual de documents digitals |
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2019 |
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Revista de biblioteconomia i documentacio |
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75-86 |
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Se analizan los sistemas de procesamiento automático que trabajan sobre documentos digitalizados con el objetivo de describir los contenidos. De esta forma contribuyen a facilitar el acceso, permitir la indización automática y hacer accesibles los documentos a los motores de búsqueda. El objetivo de estas tecnologías es poder entrenar modelos computacionales que sean capaces de clasificar, agrupar o realizar búsquedas sobre documentos digitales. Así, se describen las tareas de clasificación, agrupamiento y búsqueda. Cuando utilizamos tecnologías de inteligencia artificial en los sistemas de
clasificación esperamos que la herramienta nos devuelva etiquetas semánticas; en sistemas de agrupamiento que nos devuelva documentos agrupados en clusters significativos; y en sistemas de búsqueda esperamos que dada una consulta, nos devuelva una lista ordenada de documentos en función de la relevancia. A continuación se da una visión de conjunto de los métodos que nos permiten describir los documentos digitales, tanto de manera visual (cuál es su apariencia), como a partir de sus contenidos semánticos (de qué hablan). En cuanto a la descripción visual de documentos se aborda el estado de la cuestión de las representaciones numéricas de documentos digitalizados
tanto por métodos clásicos como por métodos basados en el aprendizaje profundo (deep learning). Respecto de la descripción semántica de los contenidos se analizan técnicas como el reconocimiento óptico de caracteres (OCR); el cálculo de estadísticas básicas sobre la aparición de las diferentes palabras en un texto (bag-of-words model); y los métodos basados en aprendizaje profundo como el método word2vec, basado en una red neuronal que, dadas unas cuantas palabras de un texto, debe predecir cuál será la
siguiente palabra. Desde el campo de las ingenierías se están transfiriendo conocimientos que se han integrado en productos o servicios en los ámbitos de la archivística, la biblioteconomía, la documentación y las plataformas de gran consumo, sin embargo los algoritmos deben ser lo suficientemente eficientes no sólo para el reconocimiento y transcripción literal sino también para la capacidad de interpretación de los contenidos. |
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DAG; 600.084; 600.135; 600.121; 600.129 |
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Admin @ si @ Rus2019 |
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3282 |
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Author |
Mohammed Al Rawi; Ernest Valveny; Dimosthenis Karatzas |
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Title |
Can One Deep Learning Model Learn Script-Independent Multilingual Word-Spotting? |
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Conference Article |
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2019 |
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15th International Conference on Document Analysis and Recognition |
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260-267 |
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Word spotting has gained increased attention lately as it can be used to extract textual information from handwritten documents and scene-text images. Current word spotting approaches are designed to work on a single language and/or script. Building intelligent models that learn script-independent multilingual word-spotting is challenging due to the large variability of multilingual alphabets and symbols. We used ResNet-152 and the Pyramidal Histogram of Characters (PHOC) embedding to build a one-model script-independent multilingual word-spotting and we tested it on Latin, Arabic, and Bangla (Indian) languages. The one-model we propose performs on par with the multi-model language-specific word-spotting system, and thus, reduces the number of models needed for each script and/or language. |
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Sydney; Australia; September 2019 |
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ICDAR |
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DAG; 600.129; 600.121 |
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Admin @ si @ RVK2019 |
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3337 |
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Mohamed Ali Souibgui; Asma Bensalah; Jialuo Chen; Alicia Fornes; Michelle Waldispühl |
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A User Perspective on HTR methods for the Automatic Transcription of Rare Scripts: The Case of Codex Runicus Just Accepted |
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2023 |
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ACM Journal on Computing and Cultural Heritage |
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JOCCH |
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15 |
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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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Mohamed Ali Souibgui; Ali Furkan Biten; Sounak Dey; Alicia Fornes; Yousri Kessentini; Lluis Gomez; Dimosthenis Karatzas; Josep Llados |
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Title |
One-shot Compositional Data Generation for Low Resource Handwritten Text Recognition |
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2022 |
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Winter Conference on Applications of Computer Vision |
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Document Analysis |
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Low resource Handwritten Text Recognition (HTR) is a hard problem due to the scarce annotated data and the very limited linguistic information (dictionaries and language models). This appears, for example, in the case of historical ciphered manuscripts, which are usually written with invented alphabets to hide the content. Thus, in this paper we address this problem through a data generation technique based on Bayesian Program Learning (BPL). Contrary to traditional generation approaches, which require a huge amount of annotated images, our method is able to generate human-like handwriting using only one sample of each symbol from the desired alphabet. After generating symbols, we create synthetic lines to train state-of-the-art HTR architectures in a segmentation free fashion. Quantitative and qualitative analyses were carried out and confirm the effectiveness of the proposed method, achieving competitive results compared to the usage of real annotated data. |
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Virtual; January 2022 |
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WACV |
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DAG; 602.230; 600.140 |
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Admin @ si @ SBD2022 |
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3615 |
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Mohamed Ali Souibgui; Sanket Biswas; Sana Khamekhem Jemni; Yousri Kessentini; Alicia Fornes; Josep Llados; Umapada Pal |
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DocEnTr: An End-to-End Document Image Enhancement Transformer |
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Conference Article |
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2022 |
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26th International Conference on Pattern Recognition |
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1699-1705 |
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Degradation; Head; Optical character recognition; Self-supervised learning; Benchmark testing; Transformers; Magnetic heads |
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Document images can be affected by many degradation scenarios, which cause recognition and processing difficulties. In this age of digitization, it is important to denoise them for proper usage. To address this challenge, we present a new encoder-decoder architecture based on vision transformers to enhance both machine-printed and handwritten document images, in an end-to-end fashion. The encoder operates directly on the pixel patches with their positional information without the use of any convolutional layers, while the decoder reconstructs a clean image from the encoded patches. Conducted experiments show a superiority of the proposed model compared to the state-of the-art methods on several DIBCO benchmarks. Code and models will be publicly available at: https://github.com/dali92002/DocEnTR |
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August 21-25, 2022 , Montréal Québec |
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ICPR |
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DAG; 600.121; 600.162; 602.230; 600.140 |
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Admin @ si @ SBJ2022 |
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3730 |
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