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The segmentation of complex images into semantic regions has seen a growing interest these last years with the advent of Deep Learning. Until recently, most existing methods for Historical Document Analysis focused on the visual appearance of documents, ignoring the rich information that textual content can offer. However, the segmentation of complex documents into semantic regions is sometimes impossible relying only on visual features and recent models embed both visual and textual information. In this paper, we focus on the use of both visual and textual information for segmenting historical registers into structured and meaningful units such as acts. An act is a text recording containing valuable knowledge such as demographic information (baptism, marriage or death) or royal decisions (donation or pardon). We propose a simple pipeline to enrich document images with the position of text lines containing key-phrases and show that running a standard image-based layout analysis system on these images can lead to significant gains. Our experiments show that the detection of acts increases from 38 % of mAP to 74 % when adding textual information, in real use-case conditions where text lines positions and content are extracted with an automatic recognition system.

Unconstrained handwriting recognition is an essential task in document analysis. It is usually carried out in two steps. First, the document is segmented into text lines. Second, an Optical Character Recognition model is applied on these line images. We propose the Simple Predict & Align Network: an end-to-end recurrence-free Fully Convolutional Network performing OCR at paragraph level without any prior segmentation stage. The framework is as simple as the one used for the recognition of isolated lines and we achieve competitive results on three popular datasets: RIMES, IAM and READ 2016. The proposed model does not require any dataset adaptation, it can be trained from scratch, without segmentation labels, and it does not require line breaks in the transcription labels. Our code and trained model weights are available at https://github.com/FactoDeepLearning/SPAN.

In this paper, we introduce a new modeling approach of texts for handwriting recognition based on syllables. We propose a supervised syllabification approach for the French and English languages for building a vocabulary of syllables. Statistical n-gram language models of syllables are trained on French and English Wikipedia corpora. The handwriting recognition system, based on optical HMM context independent character models, performs a two pass decoding, integrating the proposed syllabic models. Evaluation is carried out on the French RIMES dataset and English IAM dataset by analyzing the performance for various coverage of the syllable models. We also compare the syllable models with lexicon and character n-gram models. The proposed approach reaches interesting performances thanks to its capacity to cover a large amount of out of vocabulary words working with a limited amount of syllables combined with statistical n-gram of reasonable order.

International audience; Handwriting recognition always has been a difficult problem, with image related problems on the one hand and language processing on the other hand. Significant improvements have been made in handwriting recognition thanks to new recurrent neural networks based on LSTM cells. The high character recognition performances of these networks are almost systematically combined with linguistic knowledge, that is to say lexicon driven decoding method, to correct character misrecognitions. However with such high performance, we wonder on the possibility to use them without lexical decoding for word recognition. In this article, we explore this idea by proposing a lexicon verification strategy that provides a very low error rate, while conceding a consequent amount of rejects. Therefore, this verification approach perfectly fits in a cascade framework, where the rejects of a classifier are processed by the next cascade's classifier. The resulting system is nearly insensitive to the lexicon size, while providing a much faster decoding process than a standard lexicon driven decoding. Furthermore, when processing the final rejects of the cascade by a basic lexical decoding, our approach reach state of the art performance for isolated word recognition.

RÉSUMÉ. Dans cet article nous introduisons un nouveau modèle syllabique pour la reconnaissance de l'écriture. Une méthode de syllabation orthographique supervisée du Français est proposé pour la construction d’un vocabulaire de syllabes. Un modèle de langage statistique en n-gram combinant syllabes et caractères est appris sur un corpus Wikipedia. Le système de reconnaissance d’écriture fondé sur des modèles optiques HMM de caractères procède à un décodage en deux passes en exploitant le modèle syllabique proposé. L’évaluation est réalisée sur la base RIMES en analysant les performances pour différents taux de couverture du modèle syllabique. Nous comparons le modèle proposé à un modèle lexical ainsi qu’à un modèle de caractères. L’approche proposée permet d’atteindre des performances intéressantes grâce à sa capacité à couvrir une proportion importante des mots hors lexique en travaillant avec un lexique de syllabes de taille limitée combiné à un modèle de n-gram d’ordre raisonnable.

RÉSUMÉ. Dans cet article, nous présentons les résultats obtenus par un détecteur de tableau dans le cadre des campagnes MAURDOR, pour lesquelles le corpus présente la particularité de contenir des document fortement hétérogènes dans leur mise en page, leurs scripts et les langues utilisées.