32 research outputs found
Vers une approche discriminante pour la reconnaissance de mots manuscrits en-ligne utilisant des modèles de bi-caractères
No full textWith the advent of mobile devices such as tablets and smartphones over the last decades, on-line handwriting recognition has become a very highly demanded service for daily life activities and professional applications. This thesis presents a new approach for on-line handwriting recognition. This approach is based on explicit segmentation/recognition integrated in a two level analysis system: character and bi-character. More specifically, our system segments a handwritten word in a sequence of graphemes to be then used to create a L-levels lattice of graphemes. Each node of the lattice is considered as a character to be submitted to a SVM based Isolated Character Recognizer (ICR). The ICR returns a list of potential character candidates, each of which is associated with an estimated recognition probability. However, each node of the lattice is a combination of various segmented graphemes. As a consequence, a node may contain some ambiguous information that cannot be handled by the ICR at character level analysis. We propose to solve this problem using "bi-character" models based on Logistic Regression, in order to verify the consistency of the information at a higher level of analysis. Finally, the recognition results provided by the ICR and the bi-character models are used in the word decoding stage, whose role is to find the optimal path in the lattice associated to each word in the lexicon. Two methods are presented for word decoding (heuristic search and dynamic programming), and dynamic programming is found to be the most effective.Avec l’avènement des dispositifs nomades tels que les smartphones et les tablettes, la reconnaissance automatique de l’écriture manuscrite cursive à partir d’un signal en ligne est devenue durant les dernières décennies un besoin réel de la vie quotidienne à l’ère numérique. Dans le cadre de cette thèse, nous proposons de nouvelles stratégies pour un système de reconnaissance de mots manuscrits en-ligne. Ce système se base sur une méthode collaborative segmentation/reconnaissance et en utilisant des analyses à deux niveaux : caractère et bi-caractères. Plus précisément, notre système repose sur une segmentation de mots manuscrits en graphèmes afin de créer un treillis à L niveaux. Chaque noeud de ce treillis est considéré comme un caractère potentiel envoyé à un moteur de Reconnaissance de Caractères Isolés (RCI) basé sur un SVM. Pour chaque noeud, ce dernier renvoie une liste de caractères associés à une liste d’estimations de probabilités de reconnaissance. Du fait de la grande diversité des informations résultant de la segmentation en graphèmes, en particulier à cause de la présence de morceaux de caractères et de ligatures, l’injection de chacun des noeuds du treillis dans le RCI engendre de potentielles ambiguïtés au niveau du caractère. Nous proposons de lever ces ambiguïtés en utilisant des modèles de bi-caractères, basés sur une régression logistique dont l’objectif est de vérifier la cohérence des informations à un niveau de reconnaissance plus élevé. Finalement, les résultats renvoyés par le RCI et l’analyse des modèles de bi-caractères sont utilisés dans la phase de décodage pour parcourir le treillis dans le but de trouver le chemin optimal associé à chaque mot dans le lexique. Deux méthodes de décodage sont proposées (recherche heuristique et programmation dynamique), la plus efficace étant basée sur de la programmation dynamique
Vers une approche discriminante pour la reconnaissance de mots manuscrits en-ligne utilisant des modèles de bi-caractères
No full textWith the advent of mobile devices such as tablets and smartphones over the last decades, on-line handwriting recognition has become a very highly demanded service for daily life activities and professional applications. This thesis presents a new approach for on-line handwriting recognition. This approach is based on explicit segmentation/recognition integrated in a two level analysis system: character and bi-character. More specifically, our system segments a handwritten word in a sequence of graphemes to be then used to create a L-levels lattice of graphemes. Each node of the lattice is considered as a character to be submitted to a SVM based Isolated Character Recognizer (ICR). The ICR returns a list of potential character candidates, each of which is associated with an estimated recognition probability. However, each node of the lattice is a combination of various segmented graphemes. As a consequence, a node may contain some ambiguous information that cannot be handled by the ICR at character level analysis. We propose to solve this problem using "bi-character" models based on Logistic Regression, in order to verify the consistency of the information at a higher level of analysis. Finally, the recognition results provided by the ICR and the bi-character models are used in the word decoding stage, whose role is to find the optimal path in the lattice associated to each word in the lexicon. Two methods are presented for word decoding (heuristic search and dynamic programming), and dynamic programming is found to be the most effective.Avec l’avènement des dispositifs nomades tels que les smartphones et les tablettes, la reconnaissance automatique de l’écriture manuscrite cursive à partir d’un signal en ligne est devenue durant les dernières décennies un besoin réel de la vie quotidienne à l’ère numérique. Dans le cadre de cette thèse, nous proposons de nouvelles stratégies pour un système de reconnaissance de mots manuscrits en-ligne. Ce système se base sur une méthode collaborative segmentation/reconnaissance et en utilisant des analyses à deux niveaux : caractère et bi-caractères. Plus précisément, notre système repose sur une segmentation de mots manuscrits en graphèmes afin de créer un treillis à L niveaux. Chaque noeud de ce treillis est considéré comme un caractère potentiel envoyé à un moteur de Reconnaissance de Caractères Isolés (RCI) basé sur un SVM. Pour chaque noeud, ce dernier renvoie une liste de caractères associés à une liste d’estimations de probabilités de reconnaissance. Du fait de la grande diversité des informations résultant de la segmentation en graphèmes, en particulier à cause de la présence de morceaux de caractères et de ligatures, l’injection de chacun des noeuds du treillis dans le RCI engendre de potentielles ambiguïtés au niveau du caractère. Nous proposons de lever ces ambiguïtés en utilisant des modèles de bi-caractères, basés sur une régression logistique dont l’objectif est de vérifier la cohérence des informations à un niveau de reconnaissance plus élevé. Finalement, les résultats renvoyés par le RCI et l’analyse des modèles de bi-caractères sont utilisés dans la phase de décodage pour parcourir le treillis dans le but de trouver le chemin optimal associé à chaque mot dans le lexique. Deux méthodes de décodage sont proposées (recherche heuristique et programmation dynamique), la plus efficace étant basée sur de la programmation dynamique
ICDAR2015 competition on smartphone document capture and OCR (SmartDoc) - Challenge 2
No full textICDAR2015 competition on smartphone document capture and OCR (SmartDoc)
Challenge 2: MOBILE OCR COMPETITION
The goal of the competition is to extract the textual content from document images which are captured by mobile phones. The images are taken under varying conditions to provide a challenging input. The dataset was prepared for ICDAR2015-SmartDoc competition. For more details about the dataset please visit the competition's website:
https://sites.google.com/site/icdar15smartdoc/home
http://smartdoc.univ-lr.fr
You may also refer to the following paper for more details on the ICDAR2015-SmartDoc competition:
Jean-Christophe Burie, Joseph Chazalon, Mickaël Coustaty, Sébastien Eskenazi, Muhammad Muzzamil Luqman, Maroua Mehri, Nibal Nayef, Jean-Marc OGIER, Sophea Prum and Marçal Rusinol: “ICDAR2015 Competition on Smartphone Document Capture and OCR (SmartDoc)”, In 13th International Conference on Document Analysis and Recognition (ICDAR), 2015.
If you use this dataset, please send us a short email at to tell us why it was useful to you, and whether you have results or publications we can reference on our website. Thank you!</p
SmartDoc-QA: A dataset for quality assessment of smartphone captured document images - single and multiple distortions
No full textModern smartphones have a revolutionary impact on the way people digitize the paper documents. The wide ownership of smartphones and their ease of use for digitizing paper documents has resulted into massive amount of imagery data of digitized paper documents. The goal of digitizing the paper documents is not only to archive them for sharing but also, most of the times, to process them by automated document image processing systems. The latter extracts the content of the document images for recognizing it, indexing it, verifying it, comparing it with a database etc. However, it is a known fact that the cameras of the smartphones are optimized for capturing natural scene images. Taking a simple photo of a paper document does not ensure that its content would be exploitable by automated document image processing systems. This could happen because of the light conditions, the resolution of the image, the camera noise, the perspective distortion, the physical distortions (folds etc.) of the paper, the out-of-focus blur and/or the motion blur during capture. To ensure that the content of a captured document image is exploitable by automated systems, it is important to automatically assess the quality of a captured document image in real-time. Otherwise most of the times it is not possible to re-capture the document image later on, because the original document is not available anymore. Assessing the quality of a captured document image is also required in situations where the captured document images are to-be transmitted for further processing.
The quality assessment step is an important part of both the acquisition and the digitization processes. Assessing document quality could aid users during the capture process or help improve image enhancement methods after a document has been captured. Current state-of-the-art works lack databases in the field of document image quality assessment.
In order to provide a baseline benchmark for quality assessment methods for mobile captured documents, we present a database for quality assessment that contains both single- and multiply-distorted document images.
The proposed dataset could be used for benchmarking quality assessment methods by the objective measure of OCR accuracy, and could be also used to benchmark quality enhancement methods. There are three types of documents in the dataset: modern documents, old administrative letters and receipts.
The document images of the dataset are captured under varying capture conditions (light, different types of blur and perspective angles). This causes geometric and photometric distortions that hinder the OCR process.
The ground truth of the dataset set images consists of the text transcriptions of the documents, the OCR results of the captured documents and the values of the different capture parameters used for each image.
Any use of this dataset is required to cite the following reference:
Nibal Nayef, Muhammad Muzzamil Luqman, Sophea Prum, Sebastien Eskenazi, Joseph Chazalon, Jean-Marc Ogier: “SmartDoc-QA: A Dataset for Quality Assessment of Smartphone Captured Document Images - Single and Multiple Distortions”, Proceedings of the sixth international workshop on Camera Based Document Analysis and Recognition (CBDAR), 2015
On-line cursive handwriting characterization using TF-IDF scores of graphemes
No full textInternational audienceIn this paper, we present an approach for characterizing the on-line cursive handwriting of different writers, which may consist in identifying the writer or his handwriting style. This method is inspired from information retrieval methods and is designed to be embedded in an adaptive word recognizer. We perform experiments assessing the effectiveness of the proposed method for writer identification. Additional preliminary experiments also show that the handwriting style can be used to personalize our cursive word recognizer, enabling the word recognition rates to be increased significantly even with a basic adaptive scheme, which is very encouraging
On-line Handwriting word recognition using a bi-character model
No full textInternational audienceThis paper deals with on-line handwriting recognition. Analytic approaches have attracted an increasing interest during the last ten years. These approaches rely on a preliminary segmentation stage, which remains one of the most difficult problems and may affect strongly the quality of the global recognition process. In order to circumvent this problem, this paper introduces a bi-character model, where each character is recognized jointly with its neighboring characters. This model yields two main advantages. First, it reduces the number of confusions due to connections between characters during the character recognition step. Second, it avoids some possible confusion at the character recognition level during the word recognition stage. Our experimentation on significant databases shows some interesting improvements of the recognition rate, since the recognition rate is increased from 65% to 83% by using this bi-character strategy
Cursive on-line Handwriting word recognition using a bi-character model for large lexicon applications
No full textInternational audienceThis paper deals with on-line handwriting recognition in a closed-world environment with a large lexicon. Several applications using handwriting recognition have been developed, but most of them consider a lexicon of limited size. Many difficulties, in particular confusions during the segmentation stage, are linked to the use of a large lexicon, with large writing variations and an increased complexity of the connections between characters. In order to circumvent these problems, we introduce in this paper an original method based on a new analytical approach using two levels of recognition models: an isolated character recognizer and an original bi-character recognition model. The idea behind the bi-character model is to recognize jointly two neighboring characters. The objective is to reduce the confusions between characters occurring during the segmentation step. Experiments show an interesting improvement of the recognition rate when introducing the bi-character model, as the recognition rate is increased of 7.2% for a 1000 words lexicon, of 9.1% for a 2000 words lexicon, and up to 15% for a 10000 words lexicon
On-line Handwriting word recognition using a bi-character model
No full textInternational audienceThis paper deals with on-line handwriting recognition. Analytic approaches have attracted an increasing interest during the last ten years. These approaches rely on a preliminary segmentation stage, which remains one of the most difficult problems and may affect strongly the quality of the global recognition process. In order to circumvent this problem, this paper introduces a bi-character model, where each character is recognized jointly with its neighboring characters. This model yields two main advantages. First, it reduces the number of confusions due to connections between characters during the character recognition step. Second, it avoids some possible confusion at the character recognition level during the word recognition stage. Our experimentation on significant databases shows some interesting improvements of the recognition rate, since the recognition rate is increased from 65% to 83% by using this bi-character strategy
