This paper presents a fuzzy visibility graph representation for handwritten mathematical expressions (HME) computed over segmented symbols using learned fuzzy landscape (FL) models. The learned FL models define the relative positioning of a pair of symbols using both their morphology, their typology and their context. A Random Forest Classifier uses this relative positioning to qualify relationships between symbols. The valued fuzzy visibility graph with the FL membership is produced from this classifier's output. This graph offers an explicit representation of the HME bi-dimensional structure which is then parsed with a set of rules to produce the recognized HME. We evaluate the performance of this system on the task of HME structure recognition using provided segmented symbols with experimental results on both CROHME 2014 and 2016 datasets. We obtain results up to par with the state-of-the-art thus proving that our fuzzy visibility graphs are a strong representation for mathematical expression parsing.

In this paper a multi-classifier method for early recognition of handwritten gesture is presented. Unlike the other works which study the early recognition problem related to the time, we propose to make the recognition according to the quantity of incremental drawing of handwritten gestures. We train a segment length based multi-classifier for the task of recognizing the handwritten touch gesture as early as possible. To deal with potential similar parts at the beginning of different gestures, we introduce a reject option to postpone the decision until ambiguity persists. We report results on two freely available datasets: MGSet and ILG. These results demonstrate the improvement we obtained by using the proposed reject option for the early recognition of handwritten gestures.

This paper presents a new method to help users defining personalized gesture commands (on pen-based devices) that maximize recognition performance from the classifier. The use of gesture commands give rise to a cross-learning situation where the user has to learn and memorize the command gestures and the classifier has to learn and recognize drawn gestures. The classification task associated with the use of customized gesture commands is complex because the classifier only has very few samples per class to start learning from. We thus need an evolving recognition system that can start from scratch or very few data samples and that will learn incrementally to achieve good performance after some using time. Our objective is to make the user aware of the recognizer difficulties during the definition of commands, by detecting confusion among gesture classes, in order to help him define a gesture set that yield good recognition performance from the beginning. To detect confusing classes we apply confusion reject principles to our evolving recognizer, which is based on a first order fuzzy inference system. A realistic experiment has been made on 55 persons to validate our confusion detection technique, and it shows that our method leads to a significant improvement of the classifier recognition performance.

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.

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.

Relative positioning between components of a structured object plays a key role for its interpretation. Fuzzy relative positioning templates are a description framework for 2D handwritten patterns, that is based on positioning models specifically designed for dealing with variability and imprecision of handwriting. In this work, we present fuzzy positioning templates and investigate the idea of recognizing structured handwritten symbols by considering the relative positioning of the components, rather than the shapes of the components themselves or the global shape of the symbol. The templates are automatically trained from data without requiring any prior knowledge. Experiments on a database of on-line symbols prove that this original strategy is a promising approach for interpretation of structured patterns.

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.

In this paper, we present a new incremental learning strategy for handwritten character recognition systems.This learning strategy enables the recognition system to learn ldquorapidlyrdquo any new character from very few examples.The presented strategy is driven by a confusion detection mechanism in order to control the learning process. Artificial characters generation techniques are used to overcome the problem of lack of learning data when introducing a new character from unseen class. The results show that a good recognition rate (about 90%) is achieved after only 5 learning examples. Moreover, the rate quickly rises to 94% after 10 examples, and approximately 97% after 30 examples. A reduction of error of 40% is obtained by using the artificial characters generation techniques.

In this paper, we present a new method for on-line Chinese character recognition that relies on an explicit description of characters structure. Contrary to most of known structural approaches, this model can describe characters written in a fluent style, thanks to a flexible fuzzy modeling of shapes and positioning of their structural components (primitives and radicals). We designed a process for incremental training of the models cooperated with automatic structural labeling for minimizing the required manual task in model design. First experiments show that the method is able to recognize non-regularly written characters and has a convincing generalization ability.

In this paper we investigate the use of linguistic information given by language models to deal with word recognition errors on handwritten sentences. We focus especially on errors due to out-of-vocabulary (OOV) words. First, word posterior probabilities are computed and used to detect error hypotheses on output sentences. An SVM classifier allows these errors to be categorized according to defined types. Then, a post-processing step is performed using a language model based on part-of-speech (POS) tags which is combined to the n-gram model previously used. Thus, error hypotheses can be further recognized and POS tags can be assigned to the OOV words. Experiments on on-line handwritten sentences show that the proposed approach allows a significant reduction of the word error rate.

In this paper we investigate the integration of a confusion network into an on-line handwritten sentence recognition system. The word posterior probabilities from the confusion network are used as confidence scored to detect potential errors in the output sentence from the Maximum A Posteriori decoding on a word graph. Dedicated classifiers (here, SVMs) are then trained to correct these errors and combine the word posterior probabilities with other sources of knowledge. A rejection phase is also introduced in the detection process. Experiments on handwritten sentences show a 28.5 % relative reduction of the word error rate.

In this paper, we present an approach for separating text and non-text ink strokes in online handwritten Japanese documents based on Markov random fields (MRFs), which effectively utilize the spatial relationship between strokes. Support vector machine (SVM) classifiers are trained for individual stroke and stroke pair classification, and on converting the SVM outputs to probabilities, the likelihood clique potentials of MRF are derived. In experiments on the TUAT Kon-date database, the proposed MRF approach yield superior performance compared to individual stroke classification and sequence classification based on hidden Markov models (HMMs).

In this paper, we present a new approach that explicitly exploits the spatial context of strokes to drive the shape recognition. We call this recognition method "context driven recognition" (CDR). The underlying idea is that only a sub-set of all possible symbols can be recognized in a specific spatial context. The main challenge is to detect and model automatically the context areas of interest so that the recognition method can be independent of any specific information on the targeted pen-based application. The paper details the learning scheme of the CDR method and how the obtained model is used during the recognition process. The results on a real-world pen-based recognition problem show that the method can reach better performances than a classical approach by decreasing the shape recognition complexity.

We present an automatic on-line adaptation mechanism to the writer's handwriting style for the recognition of isolated handwritten characters. The classifier is based on a fuzzy inference system (FIS). This FIS is composed of fuzzy prototypes which represent the intrinsic properties of the classes and it uses numeric conclusions. The proposed adaptation mechanism affects both the conclusions of the rules and the fuzzy prototypes of the premises by re-centering and re-shaping them. Doing so, the FIS is automatically fitted to the handwriting style of the writer that is currently using the system. This adaptation mechanism has been tested with 8 different writers. The results show the adaptation mechanism is able to improve the recognition rate from 88% to 98.2% in average for the 26 Latin letters.

This paper investigates the integration of a statistical language model into an on-line recognition system in order to improve word recognition in the context of handwritten sentences. Two kinds of models have been considered: n-gram and n-class models (with a statistical approach to create word classes). All these models are trained over the Susanne corpus and experiments are carried out on sentences from this corpus which were written by several writers. The use of a statistical language model is shown to improve the word recognition rate and the relative impact of the different language models is compared. Furthermore, we illustrate the interest to define an optimal cooperation between the language model and the recognition system to re-enforce the accuracy of the system.

This paper presents a method to generate an equivalent on-line signal of a letter image. To do this, an off-line drawing order has to be recovered using handwriting knowledge. Our approach consists in looking for several start and the end points. Then a reconstruction algorithm is applied and finally the best path is chosen. This approach has been validated on a database of handwritten letters including both on-line and off-line signals. An on-line recognition system has been used to recognize these letters.

This paper presents a lexical post-processing optimization for handwritten word recognition. The aim of this work is to explore the combination of different lexical post-processing approaches in order to optimize the recognition rate, the recognition time and memory requirements. The present method focuses on the following tasks: a lexicon organization with word filtering, based on holistic word features to deal with large vocabulary (creation of static sublexicon compressed in a tree structure); a dedicated string matching algorithm for online handwriting (to compensate for the recognition and the segmentation errors); and a specific exploration strategy of the results provided by the analytical word recognition process. Experimental results are reported using several lexicon sizes (about 1000, 7000 and 25000 entries) to evaluate different optimization strategies according to the recognition rate, computational cost and memory requirements.

In our previous works, a recognition system named ResifCar was designed specifically for on-line handwritten character recognition. This system is based on an explicit modeling by hierarchical fuzzy rules. Thus, it is understandable an optimizable after the learning stage. We present in this article a new classifier that is an extension of ResifCar. Indeed it tries to combine ResifCar's advantages with a generic aspect to handle different recognition problems. This new hybrid system combines two complementary levels. The first one uses a robust modeling by an intrinsic fuzzy clustering of each class and determines their confusing areas. The second level, based on fuzzy decision trees, operates a progressive discrimination inside these areas. Both levels are formalized by fuzzy inference systems organized hierarchically and fused for final decision. Experiments were conducted on the one hand on classical benchmarks and on the other hand on on-line handwritten digits and lower-case letters. For all of these cases, the classifier achieves good recognition rates without final optimization. 1.

A new handwriting modeling and segmentation approach is introduced for cursive letter and word analysis. For the letter analysis, the proposed method is based on the detection of a set of "perceptual anchorage points" to extract a priori pertinent strokes. This physical segmentation of the handwritten drawing enables us to conduct a logical modeling of letters with respect to the most stable strokes of each letter class. For the handwritten word analysis, we present a constructive segmentation approach to overcome the word segmentation problem. The main idea is to locate "anchorage structures" in the word drawing based on the most robust strokes of the letters. This new approach of handwriting analysis has been implemented in a writer-independent online handwriting recognition system. Experimental results are reported using a lexicon context of 1128, 7000 and 25,000 words.