In this article, we present our work on baseline detection in images of historical documents. This work focuses on handwritten documents containing tabular structures. One of the difficulties of this kind of documents is the strong interaction between text and tabular structures. This interaction leads to ambiguous cases for which recognition systems often over-or sub-segment baselines. The interest of our method is to combine contextual and structural knowledge in order to interpret properly this interaction. Our combination is able to merge heterogeneous information obtained with a deep-learning approach (for contextual elements) and a syntactical approach (for structural elements). Our grammatical description consists on a logical description of the intersections between text-lines and vertical rulings of detected tables. Intersections are described thanks to physical indicators extracted from images: vertical rulings, hypothetical text-lines, begin-and end-indicators of text-lines. We show on cBAD competition [4] (competition on baseline detection) that the combination of heterogeneous knowledge (structural and contextual information) improves baseline detection in handwritten documents. We obtain better scores than the best method published until now on this competition.

Dealing with non annotated documents for the design of a document recognition system is not an easy task. In general, statistical methods cannot learn without an annotated ground truth, unlike syntactical methods. However their ability to deal with non annotated data comes from the fact that the description is manually made by a user. The adaptation to a new kind of document is then tedious as the whole manual process of extraction of knowledge has to be redone. In this paper, we propose a method to extract knowledge and generate rules without any ground truth. Using large volume of non annotated documents, it is possible to study redundancies of some extracted elements in the document images. The redundancy is exploited through an automatic clustering algorithm. An interaction with the user brings semantic to the detected clusters. In this work, the extracted elements are some keywords extracted with word spotting. This approach has been applied to old marriage record field detection on the FamilySearch HIP2013 competition database. The results demonstrate that we successfully automatically infer rules from non annotated documents using the redundancy of extracted elements of the documents.

A critical step of on-line handwritten diagram recognition is the segmentation between text and symbols. It is still an open problem in several approaches of the literature. However, for a human operator, text/symbol segmentation is an easy task and does not even need understanding diagram semantics. It is done thanks to the use of both structural knowledge and statistical analysis. A human operator knows what is a symbol and how to distinguish a good symbol from a bad one in a list of candidates. We propose to reproduce this perceptive mechanism by introducing some statistical information inside of a grammatical method for document structure recognition, in order to combine both structural an statistical knowledge. This approach is applied to flowchart recognition on a freely available database. The results demonstrate the interest of combining statistical and structural information for perceptive vision in diagram recognition.

In this paper, we present the impact of user interaction in the recognition of off-line structured documents. This interaction requires solving two major problems: how interpretation results will be presented to the user, and how the user will interact with analysis process. We propose to study the effects of those two aspects in the context of an interactive method (IMISketch) for off-line handwritten 2D architectural floor plan recognition. The use tests are realized in collaboration with researchers in cognitive psychology (more than 100 persons participated in the tests). The experiments demonstrate that (i) a progressive presentation of the analysis results, (ii) user interventions during it and (iii) the user solicitation by the analysis process are an efficient strategy for interactive recognition of offline documents.

Extracting and indexing meaningful contents from degraded documents, like historical ones, is a challenging problem. Existing analysis systems usually rely on a manual correction of results during the post-processing stage, and cannot make use of external information to adapt their response. This paper presents how an existing document analysis system can be easily adapted to enable an efficient interaction during the analysis stage, and benefit from external information. We identify the minimal architecture required, and we detail the two complimentary interaction models we propose: a directed interaction model which allows to handle cases where errors can be automatically detected, and a spontaneous interaction model which permits to cope with the other cases. Both models are asynchronous to prevent the human operator or the system from waiting for each other during document processing. They are based on a common foundation which uses standard exception-like mechanisms to implement error detection, correction and recovery aspects. Our system was tested on several tasks. For instance, for the transcription of handwritten words in documents dating from the 18th century, where we were able to diminish the human workload by 28% for an overall recognition rate of 80%.

In this paper, we present an optimized approach, based on a competitive breadth-first exploration of the analysis tree, for an interactive interpretation of off-line sketch. The competitive breadth-first exploration of the analysis tree, allows to compare several hypotheses of interpretation to deal with confusion. Unfortunately, in practice these methods are rarely used because they often induce a large combinatory. This paper presents an optimization strategy to minimize the combinatory. The aim is to demonstrate the relevance of a competitive breadth-first exploration in off-line document analysis, in particular when the approach is interactive, ie the user is involved into the loop analysis. This paper demonstrates this optimized interactive analysis method on off-line handwritten 2D architectural floor plans.

The analysis of sets of degraded documents, like historical ones, is error-prone and requires human help to achieve acceptable quality levels. However, human interaction raises 3 main issues when processing important amounts of pages: none of the user or the system should wait for work, information provided by a human operator should not be restricted to local isolated corrections, but rather produce durable changes in the system, the ability to interact with a human operator should not increase the complexity of document models nor duplicate them between analysis and human interaction processes. To solve those issues, we propose an iterative approach, based on a special mechanism called visual memory, to reintegrate external information during page analysis. So as to demonstrate the interest for existing systems, we explain how we adapted a (rule-based) page analysis tool to enable, in this iterative approach, a delayed interaction with a human operator based on an adaptation of error recovery principles for compilers and the well-known exception handling mechanism. We validated our iterative approach on sales registers from the 18th century.

In this paper, we present a new generic method for an interactive interpretation of sketches. This method is based on a competitive breadth-first exploration of the analysis tree. As opposed to well known structural approaches, this method allows to evaluate simultaneously several possible hypotheses of recognition in a dynamic local context of document. At each step of the analysis, the decision process selects the best hypotheses. If it detects an ambiguity, it will solicit the user to select the right hypothesis. In fact, the user participation has a great impact to avoid error accumulation during the analysis step and overcomes the combinatory due to the sketch complexity. This paper demonstrates this interactive method on 2D architectural floor plans.

When reading a document, we intuitively have a first global approach in order to determine the whole structure, before reading parts in details. We propose to apply the same kind of mechanism by introducing the concept of multiresolution in an existing generic method for structured document recognition. This new combination of different vision levels makes it possible to recognize low structured documents. We present our work on an example: the multiresolution description of archive documents that are naturalization decree registers from the 19th and 20th century. The validation has been made on 85,088 images. Integrated in a platform for archive documents, the located elements offers to users a fast leaf through naturalization decrees.

In this paper, we propose to give tools for Bangla handwriting recognition. We present a mechanism to segment documents into text lines and words, and more specifically to detect headline position in each word. Indeed, this headline is an horizontal line on the upper part of most of characters, which is characteristic of Bangla writing. Its localisation is a new approach that can improve text recognition quality. This headline is detected into words inside text lines thanks to a notion of perceptive vision: at a certain distance, text lines appear as line-segments that give the global orientation of words. Watching closer may help to give the exact position of the headline. Consequently, this work is mainly based on applying a segment extractor at different image resolutions and combining extracted information in order to compute the headlines. Our line-segment extractor is based on Kalman filtering.