With mass of data, document image classification systems have to face new trends like being able to process heterogeneous data streams efficiently. Generally, when processing data streams, few knowledge is available about the content of the possible streams. Furthermore, as getting labelled data is costly, the classification model has to be learned from few available labelled examples. To handle such specific context, we think that combining one-class classifiers could be a very interesting alternative to quickly define and tune classification systems dedicated to different document streams. The main interest of one-class classifiers is that no interdependence occurs between each classifier model allowing easy removal, addition or modification of classes of documents. Such reconfiguration will not have any impact on the other classifiers. It is also noticeable that each classifier can use a different set of features compared to the other to handle the same class or even different classes. In return, as only one class is well-specified during the learning step, one-class classifiers have to be defined carefully to obtain good performances. It is more difficult to select the representative training examples and the discriminative features with only positive examples. To overcome these difficulties, we have defined a complete framework offering different methods that can help a system designer to define and tune one-class classifier models. The aims are to make easier the selection of good training examples and of suitable features depending on the class to recognize into the document stream. For that purpose, the proposed methods compute different measures to evaluate the relevance of the available features and training examples. Moreover, a visualization of the decision space according to selected examples and features is proposed to help such a choice and, an automatic tuning is proposed for the parameters of the models according to the class to recognize when a validation stream is available. The pertinence of the proposed framework is illustrated on two different use cases (a real data stream and a public data set).

During past years, text extraction in document images has been widely studied in the general context of Document Image Analysis (DIA) and especially in the framework of layout analysis. Many existing techniques rely on complex processes based on preprocessing, image transforms or component/edges extraction and their analysis. At the same time, text extraction inside videos has received an increased interest and the use of corner or key points has been proven to be very effective. Because it is noteworthy to notice that very few studies were performed on the use of corner points for text extraction in document images, we propose in this paper to evaluate the possibilities associated with this kind of approach for DIA. To do that, we designed a very simple technique based on FAST key points. A first stage divide the image into blocks and the density of points inside each one is computed. The more dense ones are kept as text blocks. Then, connectivity of blocks is checked to group them and to obtain complete text blocks. This technique has been evaluated on different kind of images: different languages (Telugu, Arabic, French), handwritten as well as typewritten, skewed documents, images at different resolution and with different kind and amount of noises (deformations, ink dot, bleed through, acquisition (blur, resolution)), etc. Even with fixed parameters for all such kind of documents images, the precision and recall are close or higher to 90% which makes this basic method already effective. Consequently, even if the proposed approach does not propose a breakthrough from theoretical aspects, it highlights that accurate text extraction could be achieved without complex approach. Moreover, this approach could also be easily improved to be more precise, robust and useful for more complex layout analysis.

In word spotting literature, classical DTW has been widely employed. However there exists several other improved versions of DTW along with other robust sequence matching techniques. Very few of them have been studied in the context of word spotting and this scarcity of research work is the motivation of the paper. This paper presents a comparative study of classical Dynamic Time Warping (DTW) technique and many of its improved modifications, as well as other sequence matching techniques in the context of word spotting. An experimental study on historical documents is performed to evaluate the behavior of DTW's variants and other sequence matching techniques. A detailed comparative analysis along with wide range of experimentation is performed, which shows that classical DTW remains a good choice when there are no segmentation problems and when features are very local. In case of word segmentation errors, Continuous Dynamic Programming (CDP) seems to be a better choice. This research work has introduced several other improved sequence matching algorithms in the context of word spotting, which show interesting and improved results.

In this paper, a new sequence matching algorithm called as Exemplary Sequence Cardinality (ESC) is proposed. ESC combines several abilities of other sequence matching algorithms e.g. DTW, SSDTW, CDP, FSM, MVM, OSB1. Depending on the application domain, ESC can be tuned to behave such as these different sequence matching algorithms. Its generality and robustness comes from its ability to find subsequences (as in CDP and SSDTW), to skip outliers inside the target sequences (as in MVM and FSM) and also in the query sequence (as in OSB ) and it has the ability to have many to one and one to many correspondences (as in DTW) between the elements of the query and the target sequences. It's special characteristic of skipping noisy elements from query sequence along with other afore mentioned properties gives it an edge over FSM. In case of word spotting application, the outliers skipping capability of ESC makes it less sensible to local variations in the spelling of words, and also to noise present in the query and/or in the target word images. Due to it's capability of sub-sequence matching, the ESC algorithm has the ability to retrieve a query inside a line or piece of line. Finally, its multiple matching facilities (many to one and one to many matching) is proven to be well advantageous in case of different length of target and query sequences due to the variability in scale, font, type/size factors. By experimenting on printed historical document images, we have demonstrated the interest of proposed ESC algorithm in specific cases when incorrect word segmentation and word level local variations occur regularly.

Since 2006 the national library of France (BnF) has developed many mass digitization projects on its collections. The indexation of digital documents on Gallica (the digital library of the BnF) is done through their textual content obtained thanks to service providers that use Optical Character Recognition software (OCR). The modern technologies of OCR achieve good performances on modern documents produced with uniform layout and known fonts. However, for old documents, OCR results are of lower quality. The OCR quality assessment is a real challenge for the BnF. On the one hand, due to the sequential architecture of OCR treatments, the identification of OCR errors sources is intractable. On the other hand, besides the word confidence, no additional quality information is reported in OCR outputs. In this paper, we present a study on OCR performance estimation aiming to control the quality of word transcriptions achieved by OCR. This quality assessment process has to operate without any comparison with ground truthed data. In this respect, our methodology relies on cross alignment of the OCR results with those of a secondary OCR called reference OCR. This secondary OCR provides uncertain but useful information that will be used as uncertain groundtruth. OCR performance is estimated using support vector regression. This predictor uses some global features computed on the cross-alignment results. The experimentations reported show that our estimate describes more faithfully the quality of OCR outputs than average word confidence scores that are computed by OCR. The proposed methodology can be adapted easily to various corpora by tuning the system using a training dataset of documents that have similar properties to those to be treated.

In this paper, we describe a novel and simple technique for prediction of OCR results without using any OCR. The technique uses a bag of allographs to characterize textual components. Then a support vector regression (SVR) technique is used to build a predictor based on the bag of allographs. The performance of the system is evaluated on a corpus of historical documents. The proposed technique produces correct prediction of OCR results on training and test documents within the range of standard deviation of 4.18% and 6.54% respectively. The proposed system has been designed as a tool to assist selection of corpora in libraries and specify the typical performance that can be expected on the selection.

In this paper, we investigate a specific area of document classification in which the documents come as a flow over the time. Moreover, the exact number of classes of document to deal with is not known from the beginning and could evolve over the time. To be able to perform classification task in such area, we need specific classifiers that are able to perform incremental learning and change their modeling over the time. More specifically, we are focusing our study on SVM approaches, known to perform well, and for which incremental (i-SVM) procedures exist. Nevertheless, most of them are only able to deal with a fixed number of classes. So we designed a new incremental learning procedure based on one-class SVMs. This one is able to improve its classification accuracy over the time, with the arrival of new labeled data, without performing any complete retraining. Moreover, when instances are coming with a previously unknown label (appearance of a new class), the training procedure is able to modify the classifier model to recognize this corresponding new kind of documents. To investigate this area, waiting for collecting documents images as a flow, we did first experiments on the Optical Recognition of Handwritten Digits Data Set. These experiments show that our incremental approach is able: to perform, at each time, as well as a static one-class classifier fully retrained using all previously seen data, to model very quickly and efficiently new incoming classes.

In this paper, we have presented a new and faster word retrieval approach, which is able to deal with heterogeneous document image collections. A certain amount of image features (statistical and Gabor Wavelet) are extracted, which inherently represent word's images. These features are used for generating hash table for fast retrieval of similar image from a very large image dataset. The decomposition and embedding of high-dimensional features and complex distance functions into a low-dimensional Hamming space helps to efficiently search items. However, existing methods do not apply for high-dimensional kernelized data when the underlying features' embedding for the kernel is unknown. The generalization of locality sensitive hashing (LSH) for arbitrary kernel is presented in the paper. The proposed algorithm provides sub-linear time similarity search and works for a wide class of similarity functions.

Presence of multi-oriented characters, connected characters with graphical lines, intersection of text and symbols with graphical lines/curves etc. are very common in graphical documents. As a result word spotting in graphical documents is still a challenging task that we try to solve (partially) in this paper. The proposed approach proceeds in two stages. In the first stage, recognition of isolated components is done using rotation invariant features and an SVM classifier. The characters having good recognition score and match in the query string are first selected for initial spotting. Because of structural complexity of graphical documents as well as of touching components, we may miss some of the query characters during initial spotting in some documents. In that case, based on the position, size and orientation of the recognized characters in the input document image, regions where missing characters may be located (candidate regions) are defined. In the second stage, Scale Invariant Feature Transform (SIFT) is used to find those missing characters in the candidate regions for possible spotting. Finally, using the position, size, orientation as well as intercharacter gap information of the recognized components, spotting is validated. Experimental results demonstrate that the method is efficient to locate a query word in multi-oriented and/or touching graphical documents.

Word searching and indexing in historical document collections is a challenging problem because, characters in these documents are often touching or broken due to degradation/ ageing effects. For efficient searching in such historical documents, this paper presents a novel approach towards word spotting using string matching of character primitives. We describe the text string as a sequence of primitives which consists of a single character or a part of a character. Primitive segmentation is performed analyzing text background information that is obtained by water reservoir technique. Next, the primitives are clustered using template matching and a codebook of representative primitives is built. Using this primitive codebook, the text information in the document images are encoded and stored. For a query word, we segment it into primitives and encode the word by a string of representative primitives from codebook. Finally, an approximate string matching is applied to find similar words. The matching similarity is used to rank the retrieved words. The proposed method is tested on historical books of French alphabets and we have obtained encouraging results from the experiment.

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.

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.