International audience

State-of-the-art methods for handwriting text recognition are based on deep learning approaches and language modeling that require large data sets during training. In practice, there are some applications where the system processes mono-writer documents, and would thus benefit from being trained on examples from that writer. However, this is not common to have numerous examples coming from just one writer. In this paper, we propose an approach to adapt both the optical model and the language model to a particular writer, from a generic system trained on large data sets with a variety of examples. We show the benefits of the optical and language model writer adaptation. Our approach reaches competitive results on the READ 2018 data set, which is dedicated to model adaptation to particular writers.

Deep learning approaches now provide state-of-the-art performance in many computer vision tasks such as handwriting recognition. However, the huge number of parameters of these models require big annotated training datasets which are difficult to obtain. Training neural networks with unlabeled data is one of the key problems to achieve significant progress in deep learning. In this article, we explore a new semi-supervised training strategy to train long-short term memory (LSTM) recurrent neural networks for isolated handwritten words recognition. The idea of our self-training strategy relies on the iteration of training Bidirectional LSTM recurrent neural network (BLSTM) using both labeled and unlabeled data. At each iteration the current trained network labels the unlabeled data and submit them to a very efficient "lexicon verification" rule. Verified unlabeled data are added to the labeled dataset at the end of each iteration. This verification stage has very low sensitivity to the lexicon size, and a full word coverage of the dataset is not necessary to make the semi-supervised method efficient. The strategy enables self-training with a single BLSTM and show promising results on the Rimes dataset.

In this article, we propose to benchmark the most popular methods for word spotting in handwritten documents. The benchmark includes a pure HMM approach, as well as hybrid discriminative methods MLP-HMM, CRF-HMM, RNN-HMM and BLSTM-CTC-HMM. This study enables us to observe the increase ratio of performance provided by each discriminative stage compared with the pure generative HMM approach. Moreover, we put forward the different abilities of all these discriminative stages from the simplest MLP to the most complex and current state of the art BLSTM-CTC. We also propose a more specific and original study on BLSTM-CTC, showing that when used as a lexicon-free recognizer, it can reach very interesting word-spotting performance.

This paper presents a pioneering attempt for developing a recurrent neural net based connectionist system for unconstrained Bengali offline handwriting recognition. The major challenge in configuring such a classification system for a complex script like Bengali is to effectively define the character classes. A novel way of defining character classes is introduced making the recognition problem suitable for using a recurrent model. Indeed, it has to deal with more than nine hundred character classes for which the occurrence probability is very skewed in the language. An off-the-shelf BLSTM-CTC recognizer is used. An open-source dataset is developed for unconstrained Bengali offline handwriting recognition. The dataset contains 2,338 handwritten text lines consisting of about 21,000 word. Experiment shows that with the new definition of character classes the BLSTM-CTC provides an impressive performance for unconstrained Bengali offline handwriting recognition. The character level recognition accuracy is 75.40% without doing any post-processing on the BLSTM-CTC output. Among the 24.60% character level errors, the substitution, deletion and insertion errors are 18.91%, 4.69% and 0.98%, respectively.

This paper presents a novel approach for language identification in handwritten documents. The approach is based on script identification followed by character recognition. BLSTM-CTC based handwriting recognizers are used and the OCR output is fed to a statistical language identifier for detecting the language of the input handwritten document. Documents in two scripts (Latin and Bengali) and four languages (English, French, Bengali and Assamese) are considered for evaluation. Several alternative frameworks have been explored, effects of handwriting recognition and text length on language detection have been studied. It is observed that with some empirical restrictions it is very much possible to achieve more that 80% language detection accuracy and based on the current research practical systems can be designed.

This paper presents a Document Image Analysis (DIA) system able to extract homogeneous typed and handwritten text regions from complex layout documents of various types. The method is based on two connected component classification stages that successively discriminate text/non text and typed/handwritten shapes, followed by an original block segmentation method based on white rectangles detection. We present the results obtained by the system during the first competition round of the MAURDOR campaign.

This paper presents a method to detect table regions in document images by identifying the column and row line-separators and their properties. The method employs a run-length approach to identify the horizontal and vertical lines present in the input image. From each group of intersecting horizontal and vertical lines, a set of 26 low-level features are extracted and an SVM classifier is used to test if it belongs to a table or not. The performance of the method is evaluated on a heterogeneous corpus of French, English and Arabic documents that contain various types of table structures and compared with that of the Tesseract OCR system.

In this paper, we propose a novel system for word spotting and regular expression detection in Handwritten documents. The proposed approach is lexicon-free, i.e., able to spot arbitrary keywords that are not required to be known at the training stage. Furthermore, the proposed system is segmentation-free, i.e., text lines are not required to be segmented into words. The originalities of our approach is twofold. First we propose a new filler model which allows to speed-up the decoding process. Second, we extend the methodology to search for regular expressions. The system has been evaluated on a public handwritten document database used for the 2011 ICDAR handwriting recognition competitions.

In this paper, we propose a multi-objective optimization method for SVM model selection using the well known NSGA-II algorithm. FA and FR rates are the two criteria used to find the optimal hyperparameters of a set of SVM classifiers. The proposed strategy is applied to a digit/outlier discrimination task embedded in a more global information extraction system that aims at locating and recognizing numerical fields in handwritten incoming mail documents. Experiments conducted on a large database of digits and outliers show clearly that our method compares favorably with the results obtained by a state-of-the- art mono-objective optimization technique using the classical Area Under ROC Curve criterion (AUC).