Analysis and Interpretation of Graphical Documents

International audienceThis chapter is dedicated to the analysis and the interpretation of graphical documents, and as such, builds upon many of the topics covered in other parts of this handbook. It will therefore not focus on any of the technical issues related to graphical documents, such as low level filtering and binarization, primitive extraction and vectorization as developed in Chapters 2.1 and 5.1 or symbol recognition, for instance, as developed in Chapter 5.2. These tools are put in a broader framework and threaded together in complex pipelines to solve interpretation questions. This chapter provides an overview of how analysis strategies have contributed to constructing these pipelines, how specific domain knowledge is integrated in these analyses, and which interpretation contexts have been contributed to successful approaches

Receipt Dataset for Fraud Detection

International audienceThe aim of this paper is to introduce a new dataset initially created to work on fraud detection in documents. This dataset is composed of 1969 images of receipts and the associated OCR result for each. The article details the dataset and its interest for the document analysis community. We indeed share this dataset with the community as a benchmark for the evaluation of fraud detection approaches

Automatic Matching and Expansion of Abbreviated Phrases without Context

International audienceIn many documents, like receipts or invoices, textual information is constrained by the space and organization of the document. The document information has no natural language context, and expressions are often abbreviated to respect the graphical layout, both at word level and phrase level. In order to analyze the semantic content of these types of document, we need to understand each phrase, and particularly each name of sold products. In this paper, we propose an approach to find the right expansion of abbreviations and acronyms, without context. First, we extract information about sold products from our receipts corpus and we analyze the different linguistic processes of abbreviation. Then, we retrieve a list of expanded names of products sold by the company that emitted receipts, and we propose an algorithm to pair extracted names of products with the corresponding expansions. We provide the research community with a unique document collection for abbreviation expansion

Identification aveugle d'images dégradées par un bruit additif ou multiplicatif

Dans cet article, nous nous intéressons au problème d'identification de la nature du bruit à partir de l'image observée en vue d'appliquer l'algorithme de traitement ou d'analyse le mieux approprié. Nous nous limitons ici à l'identification des bruits additifs et multiplicatifs

Find it! Fraud Detection Contest Report

International audienceThis paper describes the ICPR2018 fraud detection contest, its data set, evaluation methodology, as well as the different methods submitted by the participants to tackle the predefined tasks. Forensics research is quite a sensitive topic. Data are either private or unlabeled and most of related works are evaluated on private datasets with a restricted access. This restriction has two major consequences: results cannot be reproduced and no benchmarking can be done between every approach. This contest was conceived in order to address these drawbacks. Two tasks were proposed: detecting documents containing at least one forgery in a flow of documents and spotting and localizing these forgeries within documents. An original dataset composed of images and texts of French receipts was provided to participants. The results they obtained are presented and discussed

CHIC: Corporate Document for Visual question Answering

The massive use of digital documents due to the substantial trend of paperless initiatives confronted some companies to find ways to process thousands of documents per day automatically. To achieve this, they use automatic information retrieval (IR) allowing them to extract useful information from large datasets quickly. In order to have effective IR methods, it is first necessary to have an adequate dataset. Although companies have enough data to take into account their needs, there is also a need for a public database to compare contributions between state-of-the-art methods. Public data on the document exists as DocVQA[2] and XFUND [10], but these do not fully satisfy the needs of companies. XFUND contains only form documents while the company uses several types of documents (i.e. structured documents like forms but also semi-structured as invoices, and unstructured as emails). Compared to XFUND, DocVQA has several types of documents but only 4.5% of them are corporate documents (i.e. invoice, purchase order, etc). All of this 4.5% of documents do not meet the diversity of documents required by the company. We propose CHIC a visual question-answering public dataset. This dataset contains different types of corporate documents and the information extracted from these documents meet the right expectations of companies

A new model for graphical object description operating in the image space or in the Cosine Discrete space

In this article a new shape descriptor – based on minimal graphs – is proposed and its properties are checked through the problem of graphical symbols recognition. Recognition invariance in front shift and multi-oriented noisy object was studied in the context of small and low resolution binary images. The approach seems to have many interesting properties, even if the construction of graphs induces an expensive algorithmic cost. In order to reduce time computing an alternatively solution based on image compression concepts is provided. The recognition is realized in a compact space, namely the Cosine Discrete space. The use of blocks discrete cosine transform is discussed and justified. The experimental results led on the GREC2003 database show that the proposed method is characterized by a good discrimination power, a real robustness to noise with an acceptable time computing.Cet article propose un nouveau modèle de description d’un objet dans une image. Ce modèle s’appui sur la construction d’un arbre minimal, ses propriétés sont étudiées à travers le problème de la reconnaissance de symboles complexes. L’invariance de la reconnaissance – face aux translations et rotations de symboles dégradés – est vérifiée dans un contexte d’images binaires à faible résolution. Si les résultats sont concluant, le coût algorithmique peut être assez élevé. Une alternative consiste à exprimer l’objet cible dans l’espace Cosinus Discret (Transformation en Cosinus Discrète). La technique opère non plus dans l’espace image mais dans un espace compact où les données sont mieux décorrélées. Certains de nos choix font référence à des concepts de compression d’images. Cette piste conduit à une diminution sensible du coût tout en conservant un niveau de discrimination significatif. Ces résultats sont d’abord observés lors d’une expérience élémentaire puis confirmés par un test à moyenne échelle, mettant en jeu 500 symboles issus de la base de données Graphics Recognition – GREC2003

NAVIDOMASS: Structural-based approaches towards handling historical documents

ISSN: 1051-4651 Print ISBN: 978-1-4244-7542-1International audienceIn the context of the NAVIDOMASS project, the problematic of this paper concerns the clustering of historical document images. We propose a structural-based framework to handle the ancient ornamental letters data-sets. The contribution, firstly, consists of examining the structural (i.e. graph) representation of the ornamental letters, secondly, the graph matching problem is applied to the resulted graph-based representations. In addition, a comparison between the structural (graphs) and statistical (generic Fourier descriptor) techniques is drawn