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Utkarsh Porwal, Alicia Fornes and Faisal Shafait, eds. 2022. Frontiers in Handwriting Recognition. International Conference on Frontiers in Handwriting Recognition. 18th International Conference, ICFHR 2022. Springer. (LNCS.)
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V. Chapaprieta and Ernest Valveny. 2001. Handwritten Digit Recognition Using Point Distribution Models..
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V. Poulain d'Andecy, Emmanuel Hartmann and Marçal Rusiñol. 2018. Field Extraction by hybrid incremental and a-priori structural templates. 13th IAPR International Workshop on Document Analysis Systems.251–256.
Abstract: In this paper, we present an incremental framework for extracting information fields from administrative documents. First, we demonstrate some limits of the existing state-of-the-art methods such as the delay of the system efficiency. This is a concern in industrial context when we have only few samples of each document class. Based on this analysis, we propose a hybrid system combining incremental learning by means of itf-df statistics and a-priori generic
models. We report in the experimental section our results obtained with a dataset of real invoices.
Keywords: Layout Analysis; information extraction; incremental learning
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V.C.Kieu, Alicia Fornes, M. Visani, N.Journet and Anjan Dutta. 2013. The ICDAR/GREC 2013 Music Scores Competition on Staff Removal. 10th IAPR International Workshop on Graphics Recognition.
Abstract: The first competition on music scores that was organized at ICDAR and GREC in 2011 awoke the interest of researchers, who participated both at staff removal and writer identification tasks. In this second edition, we propose a staff removal competition where we simulate old music scores. Thus, we have created a new set of images, which contain noise and 3D distortions. This paper describes the distortion methods, metrics, the participant’s methods and the obtained results.
Keywords: Competition; Music scores; Staff Removal
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Veronica Romero, Alicia Fornes, Enrique Vidal and Joan Andreu Sanchez. 2017. Information Extraction in Handwritten Marriage Licenses Books Using the MGGI Methodology. In L.A. Alexandre, J.Salvador Sanchez and Joao M. F. Rodriguez, eds. 8th Iberian Conference on Pattern Recognition and Image Analysis.287–294. (LNCS.)
Abstract: Historical records of daily activities provide intriguing insights into the life of our ancestors, useful for demographic and genealogical research. For example, marriage license books have been used for centuries by ecclesiastical and secular institutions to register marriages. These books follow a simple structure of the text in the records with a evolutionary vocabulary, mainly composed of proper names that change along the time. This distinct vocabulary makes automatic transcription and semantic information extraction difficult tasks. In previous works we studied the use of category-based language models and how a Grammatical Inference technique known as MGGI could improve the accuracy of these tasks. In this work we analyze the main causes of the semantic errors observed in previous results and apply a better implementation of the MGGI technique to solve these problems. Using the resulting language model, transcription and information extraction experiments have been carried out, and the results support our proposed approach.
Keywords: Handwritten Text Recognition; Information extraction; Language modeling; MGGI; Categories-based language model
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Veronica Romero, Alicia Fornes, Enrique Vidal and Joan Andreu Sanchez. 2016. Using the MGGI Methodology for Category-based Language Modeling in Handwritten Marriage Licenses Books. 15th international conference on Frontiers in Handwriting Recognition.
Abstract: Handwritten marriage licenses books have been used for centuries by ecclesiastical and secular institutions to register marriages. The information contained in these historical documents is useful for demography studies and
genealogical research, among others. Despite the generally simple structure of the text in these documents, automatic transcription and semantic information extraction is difficult due to the distinct and evolutionary vocabulary, which is composed mainly of proper names that change along the time. In previous
works we studied the use of category-based language models to both improve the automatic transcription accuracy and make easier the extraction of semantic information. Here we analyze the main causes of the semantic errors observed in previous results and apply a Grammatical Inference technique known as MGGI to improve the semantic accuracy of the language model obtained. Using this language model, full handwritten text recognition experiments have been carried out, with results supporting the interest of the proposed approach.
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Veronica Romero and 7 others. 2013. The ESPOSALLES database: An ancient marriage license corpus for off-line handwriting recognition. PR, 46(6), 1658–1669.
Abstract: Historical records of daily activities provide intriguing insights into the life of our ancestors, useful for demography studies and genealogical research. Automatic processing of historical documents, however, has mostly been focused on single works of literature and less on social records, which tend to have a distinct layout, structure, and vocabulary. Such information is usually collected by expert demographers that devote a lot of time to manually transcribe them. This paper presents a new database, compiled from a marriage license books collection, to support research in automatic handwriting recognition for historical documents containing social records. Marriage license books are documents that were used for centuries by ecclesiastical institutions to register marriage licenses. Books from this collection are handwritten and span nearly half a millennium until the beginning of the 20th century. In addition, a study is presented about the capability of state-of-the-art handwritten text recognition systems, when applied to the presented database. Baseline results are reported for reference in future studies.
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Veronica Romero, Emilio Granell, Alicia Fornes, Enrique Vidal and Joan Andreu Sanchez. 2019. Information Extraction in Handwritten Marriage Licenses Books. 5th International Workshop on Historical Document Imaging and Processing.66–71.
Abstract: Handwritten marriage licenses books are characterized by a simple structure of the text in the records with an evolutionary vocabulary, mainly composed of proper names that change along the time. This distinct vocabulary makes automatic transcription and semantic information extraction difficult tasks. Previous works have shown that the use of category-based language models and a Grammatical Inference technique known as MGGI can improve the accuracy of these
tasks. However, the application of the MGGI algorithm requires an a priori knowledge to label the words of the training strings, that is not always easy to obtain. In this paper we study how to automatically obtain the information required by the MGGI algorithm using a technique based on Confusion Networks. Using the resulting language model, full handwritten text recognition and information extraction experiments have been carried out with results supporting the proposed approach.
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Volkmar Frinken, Alicia Fornes, Josep Llados and Jean-Marc Ogier. 2012. Bidirectional Language Model for Handwriting Recognition. Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshop. Springer Berlin Heidelberg, 611–619. (LNCS.)
Abstract: In order to improve the results of automatically recognized handwritten text, information about the language is commonly included in the recognition process. A common approach is to represent a text line as a sequence. It is processed in one direction and the language information via n-grams is directly included in the decoding. This approach, however, only uses context on one side to estimate a word’s probability. Therefore, we propose a bidirectional recognition in this paper, using distinct forward and a backward language models. By combining decoding hypotheses from both directions, we achieve a significant increase in recognition accuracy for the off-line writer independent handwriting recognition task. Both language models are of the same type and can be estimated on the same corpus. Hence, the increase in recognition accuracy comes without any additional need for training data or language modeling complexity.
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Volkmar Frinken, Andreas Fischer and Carlos David Martinez Hinarejos. 2013. Handwriting Recognition in Historical Documents using Very Large Vocabularies. 2nd International Workshop on Historical Document Imaging and Processing.67–72.
Abstract: Language models are used in automatic transcription system to resolve ambiguities. This is done by limiting the vocabulary of words that can be recognized as well as estimating the n-gram probability of the words in the given text. In the context of historical documents, a non-unified spelling and the limited amount of written text pose a substantial problem for the selection of the recognizable vocabulary as well as the computation of the word probabilities. In this paper we propose for the transcription of historical Spanish text to keep the corpus for the n-gram limited to a sample of the target text, but expand the vocabulary with words gathered from external resources. We analyze the performance of such a transcription system with different sizes of external vocabularies and demonstrate the applicability and the significant increase in recognition accuracy of using up to 300 thousand external words.
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