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Suman Ghosh and Ernest Valveny. 2017. R-PHOC: Segmentation-Free Word Spotting using CNN. 14th International Conference on Document Analysis and Recognition.
Abstract: arXiv:1707.01294
This paper proposes a region based convolutional neural network for segmentation-free word spotting. Our network takes as input an image and a set of word candidate bound- ing boxes and embeds all bounding boxes into an embedding space, where word spotting can be casted as a simple nearest neighbour search between the query representation and each of the candidate bounding boxes. We make use of PHOC embedding as it has previously achieved significant success in segmentation- based word spotting. Word candidates are generated using a simple procedure based on grouping connected components using some spatial constraints. Experiments show that R-PHOC which operates on images directly can improve the current state-of- the-art in the standard GW dataset and performs as good as PHOCNET in some cases designed for segmentation based word spotting.
Keywords: Convolutional neural network; Image segmentation; Artificial neural network; Nearest neighbor search
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Suman Ghosh and Ernest Valveny. 2017. Visual attention models for scene text recognition. 14th International Conference on Document Analysis and Recognition.
Abstract: arXiv:1706.01487
In this paper we propose an approach to lexicon-free recognition of text in scene images. Our approach relies on a LSTM-based soft visual attention model learned from convolutional features. A set of feature vectors are derived from an intermediate convolutional layer corresponding to different areas of the image. This permits encoding of spatial information into the image representation. In this way, the framework is able to learn how to selectively focus on different parts of the image. At every time step the recognizer emits one character using a weighted combination of the convolutional feature vectors according to the learned attention model. Training can be done end-to-end using only word level annotations. In addition, we show that modifying the beam search algorithm by integrating an explicit language model leads to significantly better recognition results. We validate the performance of our approach on standard SVT and ICDAR'03 scene text datasets, showing state-of-the-art performance in unconstrained text recognition.
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Albert Berenguel, Oriol Ramos Terrades, Josep Llados and Cristina Cañero. 2017. Evaluation of Texture Descriptors for Validation of Counterfeit Documents. 14th International Conference on Document Analysis and Recognition.1237–1242.
Abstract: This paper describes an exhaustive comparative analysis and evaluation of different existing texture descriptor algorithms to differentiate between genuine and counterfeit documents. We include in our experiments different categories of algorithms and compare them in different scenarios with several counterfeit datasets, comprising banknotes and identity documents. Computational time in the extraction of each descriptor is important because the final objective is to use it in a real industrial scenario. HoG and CNN based descriptors stands out statistically over the rest in terms of the F1-score/time ratio performance.
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ChunYang, Xu Cheng Yin, Hong Yu, Dimosthenis Karatzas and Yu Cao. 2017. ICDAR2017 Robust Reading Challenge on Text Extraction from Biomedical Literature Figures (DeTEXT). 14th International Conference on Document Analysis and Recognition.1444–1447.
Abstract: Hundreds of millions of figures are available in the biomedical literature, representing important biomedical experimental evidence. Since text is a rich source of information in figures, automatically extracting such text may assist in the task of mining figure information and understanding biomedical documents. Unlike images in the open domain, biomedical figures present a variety of unique challenges. For example, biomedical figures typically have complex layouts, small font sizes, short text, specific text, complex symbols and irregular text arrangements. This paper presents the final results of the ICDAR 2017 Competition on Text Extraction from Biomedical Literature Figures (ICDAR2017 DeTEXT Competition), which aims at extracting (detecting and recognizing) text from biomedical literature figures. Similar to text extraction from scene images and web pictures, ICDAR2017 DeTEXT Competition includes three major tasks, i.e., text detection, cropped word recognition and end-to-end text recognition. Here, we describe in detail the data set, tasks, evaluation protocols and participants of this competition, and report the performance of the participating methods.
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Arnau Baro, Pau Riba, Jorge Calvo-Zaragoza and Alicia Fornes. 2017. Optical Music Recognition by Recurrent Neural Networks. 14th IAPR International Workshop on Graphics Recognition.25–26.
Abstract: Optical Music Recognition is the task of transcribing a music score into a machine readable format. Many music scores are written in a single staff, and therefore, they could be treated as a sequence. Therefore, this work explores the use of Long Short-Term Memory (LSTM) Recurrent Neural Networks for reading the music score sequentially, where the LSTM helps in keeping the context. For training, we have used a synthetic dataset of more than 40000 images, labeled at primitive level
Keywords: Optical Music Recognition; Recurrent Neural Network; Long Short-Term Memory
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Pau Torras, Mohamed Ali Souibgui, Jialuo Chen and Alicia Fornes. 2021. A Transcription Is All You Need: Learning to Align through Attention. 14th IAPR International Workshop on Graphics Recognition.141–146. (LNCS.)
Abstract: Historical ciphered manuscripts are a type of document where graphical symbols are used to encrypt their content instead of regular text. Nowadays, expert transcriptions can be found in libraries alongside the corresponding manuscript images. However, those transcriptions are not aligned, so these are barely usable for training deep learning-based recognition methods. To solve this issue, we propose a method to align each symbol in the transcript of an image with its visual representation by using an attention-based Sequence to Sequence (Seq2Seq) model. The core idea is that, by learning to recognise symbols sequence within a cipher line image, the model also identifies their position implicitly through an attention mechanism. Thus, the resulting symbol segmentation can be later used for training algorithms. The experimental evaluation shows that this method is promising, especially taking into account the small size of the cipher dataset.
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Albert Berenguel, Oriol Ramos Terrades, Josep Llados and Cristina Cañero. 2017. e-Counterfeit: a mobile-server platform for document counterfeit detection. 14th IAPR International Conference on Document Analysis and Recognition.
Abstract: This paper presents a novel application to detect counterfeit identity documents forged by a scan-printing operation. Texture analysis approaches are proposed to extract validation features from security background that is usually printed in documents as IDs or banknotes. The main contribution of this work is the end-to-end mobile-server architecture, which provides a service for non-expert users and therefore can be used in several scenarios. The system also provides a crowdsourcing mode so labeled images can be gathered, generating databases for incremental training of the algorithms.
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Y. Patel, Lluis Gomez, Marçal Rusiñol and Dimosthenis Karatzas. 2016. Dynamic Lexicon Generation for Natural Scene Images. 14th European Conference on Computer Vision Workshops.395–410.
Abstract: Many scene text understanding methods approach the endtoend recognition problem from a word-spotting perspective and take huge bene t from using small per-image lexicons. Such customized lexicons are normally assumed as given and their source is rarely discussed.
In this paper we propose a method that generates contextualized lexicons
for scene images using only visual information. For this, we exploit
the correlation between visual and textual information in a dataset consisting
of images and textual content associated with them. Using the topic modeling framework to discover a set of latent topics in such a dataset allows us to re-rank a xed dictionary in a way that prioritizes the words that are more likely to appear in a given image. Moreover, we train a CNN that is able to reproduce those word rankings but using only the image raw pixels as input. We demonstrate that the quality of the automatically obtained custom lexicons is superior to a generic frequency-based baseline.
Keywords: scene text; photo OCR; scene understanding; lexicon generation; topic modeling; CNN
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Sounak Dey, Anjan Dutta, Suman Ghosh, Ernest Valveny and Josep Llados. 2018. Aligning Salient Objects to Queries: A Multi-modal and Multi-object Image Retrieval Framework. 14th Asian Conference on Computer Vision.
Abstract: In this paper we propose an approach for multi-modal image retrieval in multi-labelled images. A multi-modal deep network architecture is formulated to jointly model sketches and text as input query modalities into a common embedding space, which is then further aligned with the image feature space. Our architecture also relies on a salient object detection through a supervised LSTM-based visual attention model learned from convolutional features. Both the alignment between the queries and the image and the supervision of the attention on the images are obtained by generalizing the Hungarian Algorithm using different loss functions. This permits encoding the object-based features and its alignment with the query irrespective of the availability of the co-occurrence of different objects in the training set. We validate the performance of our approach on standard single/multi-object datasets, showing state-of-the art performance in every dataset.
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Jaume Gibert and Ernest Valveny. 2010. Graph Embedding based on Nodes Attributes Representatives and a Graph of Words Representation. In In E.R. Hancock, R.C.W., T. Windeatt, I. Ulusoy and F. Escolano,, ed. 13th International worshop on structural and syntactic pattern recognition and 8th international worshop on statistical pattern recognition. Springer Berlin Heidelberg, 223–232. (LNCS.)
Abstract: Although graph embedding has recently been used to extend statistical pattern recognition techniques to the graph domain, some existing embeddings are usually computationally expensive as they rely on classical graph-based operations. In this paper we present a new way to embed graphs into vector spaces by first encapsulating the information stored in the original graph under another graph representation by clustering the attributes of the graphs to be processed. This new representation makes the association of graphs to vectors an easy step by just arranging both node attributes and the adjacency matrix in the form of vectors. To test our method, we use two different databases of graphs whose nodes attributes are of different nature. A comparison with a reference method permits to show that this new embedding is better in terms of classification rates, while being much more faster.
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