Learning robotic manipulation skills from vision is a promising approach for developing robotics applications that can generalize broadly to real-world scenarios. As such, many approaches to enable this vision have been explored with fruitful results. Particularly, object-centric representation methods have been shown to provide better inductive biases for skill learning, leading to improved performance and generalization. Nonetheless, we show that object-centric methods can struggle to learn simple manipulation skills in multi-object environments.Thus, we propose DOCIR, an object-centric framework that introduces a disentangled representation for objects of interest, obstacles, and robot embodiment. We show that this approach leads to state-of-the-art performance for learning pick and place skills from visual inputs in multi-object environments and generalizes at test time to changing objects of interest and distractors in the scene. Furthermore, we show its efficacy both in simulation and zero-shot transfer to the real world.

We consider the Document Understanding problem of segmenting tables in rows. We propose a method that first enumerates virtual row separator candidates and then select the correct ones thanks to a classification task, solved using supervised structured machine learning. Interestingly, the task is the joint-classification of virtual separators and real text lines. We describe and tested several alternative candidate generation methods and report the results of our experiment for each, on two different types of registry books from the 19th century.

The cTDaR competition aims at benchmarking state-of-the-art table detection (TRACK A) and table recognition (TRACK B) methods. In particular, we wish to investigate and compare general methods that can reliably and robustly identify the table regions within a document image on the one hand, and the table structure on the other hand. Due to the presence of hand-drawn tables and handwritten text, the methods must be robust against various noise conditions, interfering annotations, and variations of the tables. Two new challenging datasets were created to test the behaviour of state-of-the-art table detection and recognition systems on real world data. One dataset consists of modern documents, while the other consists of archival documents with presence of hand-drawn tables and handwritten text. The evaluation scheme is adapted from the ICDAR 2013 Table competition. We received results of Track A from 11 teams and results of Track B from 2 teams. Results for Track A are very good for the top participants. The winner and his runner-up are very close while using very different approaches. Track B was more challenging and only one participant was able to produce good results.

Recent advances in document understanding, especially text recognition, provide new opportunities to address the page segmentation problem. In this paper, we propose a method to groups text lines into semantic objects. We model a page as a graph where nodes represent text lines and the edges their geometric relations. The logical segmentation task then refers to identify all text lines belonging to some logical sub-division of the page. We model this task as categorizing edges as relevant or not to build the targeted sub-division (sub-graph). This edge categorization is performed using structured machine learning algorithms (graph Conditional Random Field and Edge Convolutional Network). We use a connected components-based approach following the edge classification for aggregating the nodes. This simple approach shows very robust results for various layout and various page sub-division. We experiment on table segmentation into multiple sub-divisions (rows, columns, and cells) and minutes segmentation into resolutions. Our sub-division and page-layout oblivious approach shows near-par performance as compared to task dedicated approaches and even outperforms them in certain setups.

We present in this paper experiments on Table Recognition in hand-written register books. We first explain how the problem of row and column detection is modelled, and then compare two Machine Learning approaches (Conditional Random Field and Graph Convolutional Network) for detecting these table elements. Evaluation was conducted on death records provided by the Archives of the Diocese of Passau. With an F-1 score of 89, both methods provide a quality which allows for Information Extraction. Software and dataset are open source/data.

In this paper we present a template-based table structure matching using association graphs for handwritten/printed historical documents. The recognition of the table structure consisting of column and header information is the prerequisite for the subsequent row detection and handwritten text recognition used for information extraction. The table matching is done by detecting the maximum clique in an association graph, which represents the matching of the line information of the template and a document of interest. This allows for variations of widths and heights of rows and columns. The presented methodology is evaluated on historical register books (death records) of the Archive of the Diocese of Passau. The method shows a reliable detection of the structure of handwritten/printed tables with a mean cell match of 88.28%.

Machine Translation (MT) has advanced in recent years to produce better translations for clients' specific domains, and sophisticated tools allow professional translators to obtain translations according to their prior edits.We suggest that MT should be further personalized to the end-user level -the receiver or the author of the text -as done in other applications.As a step in that direction, we propose a method based on a recommender systems approach where the user's preferred translation is predicted based on preferences of similar users.In our experiments, this method outperforms a set of non-personalized methods, suggesting that user preference information can be employed to provide better-suited translations for each user.

In this paper, we summarize the 2nd Book Structure Extraction competition run at ICDAR 2011. Its goal is to evaluate and compare automatic techniques for deriving structure information from digitized books, which could then be used to aid navigation inside the books. More specifically, the task that participants are faced with is to construct hyper linked tables of contents for a collection of 1,000 digitized books. This paper reviews the setup of the competition, the book collection used in the task, and the measures used for the evaluation. It further presents the outcome of the competition: an additional ground truth of 513 book tables of contents, contributed by 6 institutions, and the result performance of the 4 participating research teams.

After two participations to the INEX competition in the Structure Extraction task, which consists in building navigation tools for digitised books by constructing hyperlinked table of contents from OCR text and layout information, we present in this paper some reflections about this competition regarding its dataset, and its evaluation measure. We point out some issues, and propose some recommendations for improving the groundtruth and the measures.

We consider here the general problem of converting documents available in print-ready or image format into a structured format that reflects the logical structure of the document. One aspect of the problem involves reconstructing conventional constructs such as titles, headings, captions, footnotes, etc. In practice, another important aspect involves putting in place some automated Quality Assessment (QA) method. We propose here a method to automate the QA in the case of a homogeneous collection by considering multiple documents at once instead of focusing only on the document being processed.

In this paper we are interested in the problem of book splitting or more generally of indexing the logical parts of a document. This involves determining the boundaries of these parts as well as their label. We report here on the combined use of generic methods published in previous papers. We discuss the effect of combining several methods, also from a quality assurance perspective. Our experiments ground on real case studies of technical documents, such as books of specifications.

We present in this paper a method for document layout analysis based on identifying the function of document elements (what they do). This approach is orthogonal and complementary to the traditional view based on the form of document elements (how they are constructed). One key advantage of such functional knowledge is that the functions of some document elements are very stable from document to document and over time. Relying on the stability of such functions, the method is not impacted by layout variability, a key issue in logical document analysis and is thus very robust and versatile. The method starts the recognition process by using functional knowledge and uses in a second step formal knowledge as a source of feedback in order to correct some errors. This allows the method to adapt to specific documents by using formal specificities.

In this paper, we present a method for structuring a document according to the information present in its Table of Contents. The detection of the ToC as well as the determination of the parts it refers to in the document body rely on a series of generic properties characterizing any ToC, while its hierarchization is achieved using clustering techniques. We also report on the robustness and performance of the method before discussing it, in light of related work.

In this paper, we propose a fast method for determining the human reading order of the layout elements of a document page. The proposal includes a computationally tractable optimization approach to the problem. We also report on the performance of the method and discuss it in light of related work.

Using a networked infrastructure of easily available sensors and context-processing components, we are developing applications for the support of workplace interactions. Notions of activity and availability are learned from labeled sensor data based on a Bayesian approach. The higher-level information on the users is then automatically derived from low-level sensor information in order to facilitate informal ad hoc communications between peer workers in an office environment.

While systems supporting communities of practice in work organizations have been shown to be desirable many, if not all, are decoupled from daily work practices and tools. This hinders a wide collection of data about their activities, because of the additional effort that is required from the users. Therefore a pre-requisite for a system aiming at making visible the community activity is the non-intrusive collection of data about the activities that are carried on in a workplace. We present a range of personal document management services that support the construction of a collective memory of user print activities. We have internally tested the system and verified that it successfully provided personal benefit, thereby ensuring that the system receives sufficient usage for the shared memory to be useful. The system also successfully addressed privacy concerns and effectively provided large data sets about document related activities. Finally it provided information able to trigger new or to reinforce existing informal exchanges in communities of practice at a convenient moment; the print action.

In this paper we present a second generation URL monitoring tool which enables the collaborative evaluation of URL content changes. In our implementation, a document monitoring agent works alongside a recommender system. Using information provided by the monitoring agent, the collaborative system alerts users when documents they are monitoring have changed. The monitoring agent provides automatic evaluation of the nature of the change. Users, however, add subjective evaluations; one user's effort informs all others monitoring the same URL. Based on these subjective evaluations, the collaborative system can filter the changed URLs, providing customized notifications for each user based on individual preferences. In this paper, we describe the implemented system and usage results.

Describes a coordination-based approach to the dynamic evolution of (software) workflow processes. Our interest is in widely distributed workflow processes, i.e. systems that allow each instance of a process model to be enacted in a distributed fashion, with different parts of the process being enacted on different nodes of the system. More specifically, we are interested in the problem of dynamic workflow process evolution in such a distributed context, where the propagation of changes to all the concerned nodes has to be performed in an orderly manner. We address the problem of dynamic workflow process evolution from a coordination system approach, considering the workflow system as a coordination system and the workflow evolution as a coordinated evolution of the coordination schemes. We illustrate the problem of workflow evolution in a software engineering context, and describe a method using the reflexive features of our underlying coordination system to support dynamic workflow process evolution in a distributed workflow system.