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Author |
Lluis Gomez; Dimosthenis Karatzas |
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Title |
A fine-grained approach to scene text script identification |
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Conference Article |
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2016 |
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12th IAPR Workshop on Document Analysis Systems |
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192-197 |
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This paper focuses on the problem of script identification in unconstrained scenarios. Script identification is an important prerequisite to recognition, and an indispensable condition for automatic text understanding systems designed for multi-language environments. Although widely studied for document images and handwritten documents, it remains an almost unexplored territory for scene text images. We detail a novel method for script identification in natural images that combines convolutional features and the Naive-Bayes Nearest Neighbor classifier. The proposed framework efficiently exploits the discriminative power of small stroke-parts, in a fine-grained classification framework. In addition, we propose a new public benchmark dataset for the evaluation of joint text detection and script identification in natural scenes. Experiments done in this new dataset demonstrate that the proposed method yields state of the art results, while it generalizes well to different datasets and variable number of scripts. The evidence provided shows that multi-lingual scene text recognition in the wild is a viable proposition. Source code of the proposed method is made available online. |
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Santorini; Grecia; April 2016 |
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DAS |
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DAG; 601.197; 600.084 |
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no |
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Admin @ si @ GoK2016b |
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2863 |
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Author |
Ernest Valveny; Enric Marti |
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Title |
Deformable Template Matching within a Bayesian Framework for Hand-Written Graphic Symbol Recognition |
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Journal Article |
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Year |
2000 |
Publication |
Graphics Recognition Recent Advances |
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1941 |
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193-208 |
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We describe a method for hand-drawn symbol recognition based on deformable template matching able to handle uncertainty and imprecision inherent to hand-drawing. Symbols are represented as a set of straight lines and their deformations as geometric transformations of these lines. Matching, however, is done over the original binary image to avoid loss of information during line detection. It is defined as an energy minimization problem, using a Bayesian framework which allows to combine fidelity to ideal shape of the symbol and flexibility to modify the symbol in order to get the best fit to the binary input image. Prior to matching, we find the best global transformation of the symbol to start the recognition process, based on the distance between symbol lines and image lines. We have applied this method to the recognition of dimensions and symbols in architectural floor plans and we show its flexibility to recognize distorted symbols. |
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Springer Verlag |
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Springer Verlag |
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DAG;IAM; |
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IAM @ iam @ MVA2000 |
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1655 |
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Author |
Andreas Fischer; Ching Y. Suen; Volkmar Frinken; Kaspar Riesen; Horst Bunke |
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Title |
A Fast Matching Algorithm for Graph-Based Handwriting Recognition |
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Conference Article |
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2013 |
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9th IAPR – TC15 Workshop on Graph-based Representation in Pattern Recognition |
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7877 |
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194-203 |
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The recognition of unconstrained handwriting images is usually based on vectorial representation and statistical classification. Despite their high representational power, graphs are rarely used in this field due to a lack of efficient graph-based recognition methods. Recently, graph similarity features have been proposed to bridge the gap between structural representation and statistical classification by means of vector space embedding. This approach has shown a high performance in terms of accuracy but had shortcomings in terms of computational speed. The time complexity of the Hungarian algorithm that is used to approximate the edit distance between two handwriting graphs is demanding for a real-world scenario. In this paper, we propose a faster graph matching algorithm which is derived from the Hausdorff distance. On the historical Parzival database it is demonstrated that the proposed method achieves a speedup factor of 12.9 without significant loss in recognition accuracy. |
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Vienna; Austria; May 2013 |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-38220-8 |
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GBR |
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DAG; 600.045; 605.203 |
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no |
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Admin @ si @ FSF2013 |
Serial |
2294 |
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Author |
Josep Llados; Enric Marti |
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Title |
A graph-edit algorithm for hand-drawn graphical document recognition and their automatic introduction into CAD systems |
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Journal Article |
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Year |
1999 |
Publication |
Machine Graphics & Vision |
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8 |
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195-211 |
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DAG;IAM; |
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IAM @ iam @ LIM1999 |
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1568 |
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Author |
Partha Pratim Roy; Umapada Pal; Josep Llados |
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Title |
Touching Text Character Localization in Graphical Documents using SIFT |
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2010 |
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Graphics Recognition. Achievements, Challenges, and Evolution. 8th International Workshop, GREC 2009. Selected Papers |
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6020 |
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199-211 |
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Keywords |
Support Vector Machine; Text Component; Graphical Line; Document Image; Scale Invariant Feature Transform |
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Interpretation of graphical document images is a challenging task as it requires proper understanding of text/graphics symbols present in such documents. Difficulties arise in graphical document recognition when text and symbol overlapped/touched. Intersection of text and symbols with graphical lines and curves occur frequently in graphical documents and hence separation of such symbols is very difficult.
Several pattern recognition and classification techniques exist to recognize isolated text/symbol. But, the touching/overlapping text and symbol recognition has not yet been dealt successfully. An interesting technique, Scale Invariant Feature Transform (SIFT), originally devised for object recognition can take care of overlapping problems. Even if SIFT features have emerged as a very powerful object descriptors, their employment in graphical documents context has not been investigated much. In this paper we present the adaptation of the SIFT approach in the context of text character localization (spotting) in graphical documents. We evaluate the applicability of this technique in such documents and discuss the scope of improvement by combining some state-of-the-art approaches. |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-13727-3 |
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DAG |
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no |
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Admin @ si @ RPL2010c |
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2408 |
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Author |
Christophe Rigaud; Clement Guerin; Dimosthenis Karatzas; Jean-Christophe Burie; Jean-Marc Ogier |
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Title |
Knowledge-driven understanding of images in comic books |
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Journal Article |
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2015 |
Publication |
International Journal on Document Analysis and Recognition |
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IJDAR |
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18 |
Issue |
3 |
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199-221 |
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Document Understanding; comics analysis; expert system |
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Document analysis is an active field of research, which can attain a complete understanding of the semantics of a given document. One example of the document understanding process is enabling a computer to identify the key elements of a comic book story and arrange them according to a predefined domain knowledge. In this study, we propose a knowledge-driven system that can interact with bottom-up and top-down information to progressively understand the content of a document. We model the comic book’s and the image processing domains knowledge for information consistency analysis. In addition, different image processing methods are improved or developed to extract panels, balloons, tails, texts, comic characters and their semantic relations in an unsupervised way. |
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Springer Berlin Heidelberg |
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1433-2833 |
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DAG; 600.056; 600.077 |
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RGK2015 |
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2595 |
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Author |
Adarsh Tiwari; Sanket Biswas; Josep Llados |
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Title |
Can Pre-trained Language Models Help in Understanding Handwritten Symbols? |
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Conference Article |
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Year |
2023 |
Publication |
17th International Conference on Document Analysis and Recognition |
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14193 |
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199–211 |
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The emergence of transformer models like BERT, GPT-2, GPT-3, RoBERTa, T5 for natural language understanding tasks has opened the floodgates towards solving a wide array of machine learning tasks in other modalities like images, audio, music, sketches and so on. These language models are domain-agnostic and as a result could be applied to 1-D sequences of any kind. However, the key challenge lies in bridging the modality gap so that they could generate strong features beneficial for out-of-domain tasks. This work focuses on leveraging the power of such pre-trained language models and discusses the challenges in predicting challenging handwritten symbols and alphabets. |
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San Jose; CA; USA; August 2023 |
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ICDAR |
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DAG |
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Admin @ si @ TBL2023 |
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3908 |
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Author |
Alicia Fornes; Xavier Otazu; Josep Llados |
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Title |
Show through cancellation and image enhancement by multiresolution contrast processing |
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Conference Article |
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2013 |
Publication |
12th International Conference on Document Analysis and Recognition |
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200-204 |
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Historical documents suffer from different types of degradation and noise such as background variation, uneven illumination or dark spots. In case of double-sided documents, another common problem is that the back side of the document usually interferes with the front side because of the transparency of the document or ink bleeding. This effect is called the show through phenomenon. Many methods are developed to solve these problems, and in the case of show-through, by scanning and matching both the front and back sides of the document. In contrast, our approach is designed to use only one side of the scanned document. We hypothesize that show-trough are low contrast components, while foreground components are high contrast ones. A Multiresolution Contrast (MC) decomposition is presented in order to estimate the contrast of features at different spatial scales. We cancel the show-through phenomenon by thresholding these low contrast components. This decomposition is also able to enhance the image removing shadowed areas by weighting spatial scales. Results show that the enhanced images improve the readability of the documents, allowing scholars both to recover unreadable words and to solve ambiguities. |
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Washington; USA; August 2013 |
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1520-5363 |
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DAG; 602.006; 600.045; 600.061; 600.052;CIC |
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Admin @ si @ FOL2013 |
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2241 |
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Author |
Gemma Sanchez; Josep Llados; K. Tombre |
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Title |
A mean string algorithm to compute the average among a set of 2D shapes |
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Journal Article |
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2002 |
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Pattern Recognition Letters |
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PRL |
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23 |
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1-3 |
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203–214 |
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DAG; IF: 0.409 |
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DAG @ dag @ SLT2002 |
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275 |
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Author |
Pau Riba; Josep Llados; Alicia Fornes; Anjan Dutta |
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Title |
Large-scale graph indexing using binary embeddings of node contexts for information spotting in document image databases |
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Journal Article |
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2017 |
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Pattern Recognition Letters |
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PRL |
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87 |
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203-211 |
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Graph-based representations are experiencing a growing usage in visual recognition and retrieval due to their representational power in front of classical appearance-based representations. However, retrieving a query graph from a large dataset of graphs implies a high computational complexity. The most important property for a large-scale retrieval is the search time complexity to be sub-linear in the number of database examples. With this aim, in this paper we propose a graph indexation formalism applied to visual retrieval. A binary embedding is defined as hashing keys for graph nodes. Given a database of labeled graphs, graph nodes are complemented with vectors of attributes representing their local context. Then, each attribute vector is converted to a binary code applying a binary-valued hash function. Therefore, graph retrieval is formulated in terms of finding target graphs in the database whose nodes have a small Hamming distance from the query nodes, easily computed with bitwise logical operators. As an application example, we validate the performance of the proposed methods in different real scenarios such as handwritten word spotting in images of historical documents or symbol spotting in architectural floor plans. |
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DAG; 600.097; 602.006; 603.053; 600.121 |
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RLF2017b |
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2873 |
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