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Partha Pratim Roy, Umapada Pal and Josep Llados. 2008. Morphology Based Handwritten Line Segmentation using Foreground and Background Information. International Conference on Frontiers in Handwriting Recognition,.241–246.
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Giacomo Magnifico, Beata Megyesi, Mohamed Ali Souibgui, Jialuo Chen and Alicia Fornes. 2022. Lost in Transcription of Graphic Signs in Ciphers. International Conference on Historical Cryptology (HistoCrypt 2022).153–158.
Abstract: Hand-written Text Recognition techniques with the aim to automatically identify and transcribe hand-written text have been applied to historical sources including ciphers. In this paper, we compare the performance of two machine learning architectures, an unsupervised method based on clustering and a deep learning method with few-shot learning. Both models are tested on seen and unseen data from historical ciphers with different symbol sets consisting of various types of graphic signs. We compare the models and highlight their differences in performance, with their advantages and shortcomings.
Keywords: transcription of ciphers; hand-written text recognition of symbols; graphic signs
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Josep Llados and Gemma Sanchez. 2004. Graph Matching vs. Graph Parsing in Graphics Recognition: A Combined Approach.
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Jaume Gibert, Ernest Valveny and Horst Bunke. 2013. Embedding of Graphs with Discrete Attributes Via Label Frequencies. IJPRAI, 27(3), 1360002–1360029.
Abstract: Graph-based representations of patterns are very flexible and powerful, but they are not easily processed due to the lack of learning algorithms in the domain of graphs. Embedding a graph into a vector space solves this problem since graphs are turned into feature vectors and thus all the statistical learning machinery becomes available for graph input patterns. In this work we present a new way of embedding discrete attributed graphs into vector spaces using node and edge label frequencies. The methodology is experimentally tested on graph classification problems, using patterns of different nature, and it is shown to be competitive to state-of-the-art classification algorithms for graphs, while being computationally much more efficient.
Keywords: Discrete attributed graphs; graph embedding; graph classification
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Josep Llados, Marçal Rusiñol, Alicia Fornes, David Fernandez and Anjan Dutta. 2012. On the Influence of Word Representations for Handwritten Word Spotting in Historical Documents. IJPRAI, 26(5), 1263002–126027.
Abstract: 0,624 JCR
Word spotting is the process of retrieving all instances of a queried keyword from a digital library of document images. In this paper we evaluate the performance of different word descriptors to assess the advantages and disadvantages of statistical and structural models in a framework of query-by-example word spotting in historical documents. We compare four word representation models, namely sequence alignment using DTW as a baseline reference, a bag of visual words approach as statistical model, a pseudo-structural model based on a Loci features representation, and a structural approach where words are represented by graphs. The four approaches have been tested with two collections of historical data: the George Washington database and the marriage records from the Barcelona Cathedral. We experimentally demonstrate that statistical representations generally give a better performance, however it cannot be neglected that large descriptors are difficult to be implemented in a retrieval scenario where word spotting requires the indexation of data with million word images.
Keywords: Handwriting recognition; word spotting; historical documents; feature representation; shape descriptors Read More: http://www.worldscientific.com/doi/abs/10.1142/S0218001412630025
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Ernest Valveny and Philippe Dosch. 2006. A general framework for the evaluation of symbol recognition methods.
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Josep Llados and Dorothea Blostein. 2007. Special Issue on Graphics Recognition. Guest Editors.
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Marçal Rusiñol and Josep Llados. 2009. A Performance Evaluation Protocol for Symbol Spotting Systems in Terms of Recognition and Location Indices. IJDAR, 12(2), 83–96.
Abstract: Symbol spotting systems are intended to retrieve regions of interest from a document image database where the queried symbol is likely to be found. They shall have the ability to recognize and locate graphical symbols in a single step. In this paper, we present a set of measures to evaluate the performance of a symbol spotting system in terms of recognition abilities, location accuracy and scalability. We show that the proposed measures allow to determine the weaknesses and strengths of different methods. In particular we have tested a symbol spotting method based on a set of four different off-the-shelf shape descriptors.
Keywords: Performance evaluation; Symbol Spotting; Graphics Recognition
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Alicia Fornes, Josep Llados, Gemma Sanchez and Dimosthenis Karatzas. 2010. Rotation Invariant Hand-Drawn Symbol Recognition based on a Dynamic Time Warping Model. IJDAR, 13(3), 229–241.
Abstract: One of the major difficulties of handwriting symbol recognition is the high variability among symbols because of the different writer styles. In this paper, we introduce a robust approach for describing and recognizing hand-drawn symbols tolerant to these writer style differences. This method, which is invariant to scale and rotation, is based on the dynamic time warping (DTW) algorithm. The symbols are described by vector sequences, a variation of the DTW distance is used for computing the matching distance, and K-Nearest Neighbor is used to classify them. Our approach has been evaluated in two benchmarking scenarios consisting of hand-drawn symbols. Compared with state-of-the-art methods for symbol recognition, our method shows higher tolerance to the irregular deformations induced by hand-drawn strokes.
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Mathieu Nicolas Delalandre, Ernest Valveny, Tony Pridmore and Dimosthenis Karatzas. 2010. Generation of Synthetic Documents for Performance Evaluation of Symbol Recognition & Spotting Systems. IJDAR, 13(3), 187–207.
Abstract: This paper deals with the topic of performance evaluation of symbol recognition & spotting systems. We propose here a new approach to the generation of synthetic graphics documents containing non-isolated symbols in a real context. This approach is based on the definition of a set of constraints that permit us to place the symbols on a pre-defined background according to the properties of a particular domain (architecture, electronics, engineering, etc.). In this way, we can obtain a large amount of images resembling real documents by simply defining the set of constraints and providing a few pre-defined backgrounds. As documents are synthetically generated, the groundtruth (the location and the label of every symbol) becomes automatically available. We have applied this approach to the generation of a large database of architectural drawings and electronic diagrams, which shows the flexibility of the system. Performance evaluation experiments of a symbol localization system show that our approach permits to generate documents with different features that are reflected in variation of localization results.
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