A hierarchically combined classifier for license plate recognition

High accuracy and fast recognition speed are two requirements for real-time and automatic license plate recognition system. In this paper, we propose a hierarchically combined classifier based on an Inductive Learning Based Method and an SVM-based classification. This approach employs the inductive learning based method to roughly divide all classes into smaller groups. Then the SVM method is used for character classification in individual groups. Both start from a collection of samples of characters from license plates. After a training process using some known samples in advance, the inductive learning rules are extracted for rough classification and the parameters used for SVM-based classification are obtained. Then, a classification tree is constructed for further fast training and testing processes for SVMbased classification. Experimental results for the proposed approach are given. From the experimental results, we can make the conclusion that the hierarchically combined classifier is better than either the inductive learning based classification or the SVMbased classification in terms of error rates and processing speeds. © 2008 IEEE

Extraction and Classification of Handwritten Annotations

This article describes a method for extracting and classifying handwritten annotations on printed documents using a simple camera integrated in a lamp or a mobile phone. The ambition of such a research is to offer a seamless integration of notes taken on printed paper in our daily interactions with digital documents. Existing studies propose a classification of annotations based on their form and function. We demonstrate a method for automating such a classification and report experimental results showing the classification accuracy

Test Segmentation of MRC Document Compression and Decompression by Using MATLAB

Abstract-The mixed raster content (MRC) standard specifies a framework for document compression which can dramatically improve the compression/ quality tradeoff as compared to traditional lossy image compression algorithms. The key to MRC compression is the separation of the document into foreground and background layers, represented as a binary mask. Therefore, the resulting quality and compression ratio of a MRC document encoder is highly dependent upon the segmentation algorithm used to compute the binary mask. The incorporated multi scale framework is used in order to improve the segmentation accuracy of text with varying size. In this paper, we propose a novel multi scale segmentation scheme for MRC document encoding based on the sequential application of two algorithms. The first algorithm, cost optimized segmentation (COS), is a block wise segmentation algorithm formulated in a global cost optimization framework. The second algorithm, connected component classification (CCC), refines the initial segmentation by classifying feature vectors of connected components using a Markov random field (MRF) model. The combined COS/CCC segmentation algorithms are then incorporated into a multi scale framework in order to improve the segmentation accuracy of text with varying size

Séparation manuscrit et imprimé dans des documents administratifs complexes par utilisation de SVM et regroupement

International audienceThis paper proposes a methodology for the segmentation of printed and handwritten zones in document images. The documents are mainly of administrative type in an unconstrained industrial framework. We have to deal with a large number each day. They can come from different clients so as to their content, layout and digitization quality vary a lot. The goal is to isolate handwritten notes from the other parts, in order to apply in a second time some dedicated processing on the printed and the handwritten layers. To achieve that, we propose a four step procedure: preprocessing, geometrical layout analysis at pseudo-word level, classification using a SVM, then post-correction with context integration allowing a better quality. The classification rates are around 90% for segmenting printed, handwritten and noisy zones.Cet article propose une méthodologie pour la séparation de l'imprimé et du manuscrit dans des images de documents. Les documents à traiter sont majoritairement de type administratif dans un environnement industriel sans contrainte, à savoir une masse quotidienne et importante de pages à traiter avec une grande diversité de contenu et de qualité de numérisation. L'objectif est d'isoler toutes les annotations manuscrites afin d'effectuer par la suite des traitements spécifiques sur le plan du manuscrit et sur le plan de l'imprimé. Nous proposons une solution en plusieurs étapes qui sont: un prétraitement des images, une segmentation du contenu en "pseudo-mots", une reconnaissance par séparateur à vaste marge de la classe d'appartenance, puis une post-correction utilisant le contexte pour affiner la segmentation. Les résultats obtenus sont de l'ordre de 90% de bonne séparation entre l'imprimé, le manuscrit et le bruit

Cognitive and social effects of handwritten annotations

This article first describes a method for extracting and classifying handwritten annotations on printed documents using a simple camera integrated in a lamp. The ambition of such a research is to offer a seamless integration of notes taken on printed paper in our daily interactions with digital documents. Existing studies propose a classification of annotations based on their form and function. We demonstrate a method for automating such a classification and report experimental results showing the classification accuracy. In the second part of the article we provide a road map for conducting user-centered studies using eye-tracking systems aiming to investigate the cognitive roles and social effects of annotations. Based on our understanding of some research questions arising from this experiment, in the last part of the article we describe a social learning environment that facilitates knowledge sharing across a class of students or a group of colleagues through shared annotations

Segmentation et classification des zones d'une page de document

International audienceThis paper proposes a methodology for complex document segmentation based on textual content and shape. The textual content corresponds with printed text and it is verified by text-word analysis using dictionary and regular expressions variable that are adapted to noise. This allows knowing where the interested expressions are placed (address, phone number etc.) The non-textual content is segmented in zone considering size and distance between connected components in order to classify zones like logo, signature, and table. To make that, features are extracted like run length, Bi level Co-occurrence... This classification is based on a modified boosting method and decision trees. The modification is about the calculation of the probability to draw training data. Compare to OCRs that are able to classify text, tables and pictures, our methodology increases the performance and allows the detection of other zones like handwritten text, logo, signature, table and tampon.Cet article propose une méthode de segmentation de documents complexes en zones d'intérêt en s'appuyant à la fois sur le contenu textuel et la forme. Le contenu textuel correspond aux sorties lisibles validées par un dictionnaire et des expressions régulières adaptées aux données bruitées. Ceci permet en parallèle de localiser des textes d'intérêt (adresses, numéros de téléphone, formules de politesse, etc.). Le contenu non lisible est regroupé en régions physiques en prenant en compte la taille et l'éloignement des composantes connexes en vue de l'identification de zones spécifiques, comme des logos, des signatures et des tampons. Pour cela, des descripteurs morphologiques sont appliqués. Cette classification s'appuie sur une méthode de boosting modifiée associée à des arbres de décision. La modification a porté sur le calcul de la probabilité d'appartenance d'un individu à une classe. Par rapport à l'action actuelle des OCRs qui classent le texte, les tableaux et les images, les résultats de notre méthode accroissent non seulement ces performances mais elle permet aussi à des zones à faible consensus comme, les annotations manuscrites, les logos, les tampons et surtout les signatures d'être reconnues

Extracción de información en documentos antiguos y manuscritos

El objetivo de este proyecto es el de, partiendo de un set de imágenes de documentos como entrada, realizar una serie de procesos sobre las imágenes con el fin de poder generar un modelo de predicción basado en machine learning que sea capaz de clasificar si los elementos que aparecen en los documentos anteriormente mencionados se tratan de texto escrito a mano, impreso o si no son texto en absoluto. Para ello, se desarrollarán y utilizarán diversos programas, con los que se pretende, por un lado, aislar los elementos de texto de las imágenes y extraer información de dichos elementos, así como crear una matriz de adyacencia que los relacione, y por el otro, aplicar estos datos para entrenar un modelo de predicción que utilice Structured Support Vector Machine. Por último, para comprobar la eficacia de dicho modelo, se harán múltiples pruebas variando los distintos modos de funcionamiento que permite el algoritmo, con tal de observar en qué condiciones funciona mejor, y realizándose un estudio de los mismos.This project's objective is, starting with a set of document images as input, to carry out a series of procedures on the images with the purpose of obtaining a prediction model based on machine learning able to classify if the elements that appear on the previously mentioned documents are either handwritten text, printed text or no text at all. In order to do that, several programs will be developed and utilized, with which it is intended, on the one hand, to isolate the text elements from the images, extract information of said elements, as well as the creation of an adjacency matrix that relates them, and on the other, to apply this data to train a prediction model that uses Structured Support Vector Machine. Lastly, in order to check the efficiency of said model, multiple tests will be done modifying the various functioning modes that the algorithm allows, with the goal of observing under which conditions does it perform better, and studying the results of those tests.L'objectiu d'aquest projecte es el de, partint d'un set d'imatges de documents com a entrada, realitzar una sèrie de processos sobre les imatges amb el fi de poder generar un model de predicció basat en machine learning que sigui capaç de classificar si els elements que apareixen en els documents anteriorment esmentats es tracten de text escrit a ma, imprès, o si no son text escrit en absolut. Pera a això, es desenvoluparan i faran servir diversos programes, amb els que es pretén, d'una banda, aïllar els elements de text de les imatges i extreure informació de dits elements, així com crear una matriu d'adjacència que els relacioni, i de l'altre, aplicar aquestes dades per a entrenar un model de predicció que utilitzi Structured Support Vector Machine. Per últim, per comprovar l'eficàcia de dit model, es faran múltiples proves variant els diferents modes de funcionament que permet l'algoritme, amb l'objectiu d'observar en que condicions funciona millor, i fent-se un estudi d'aquests

CorDeep and the Sacrobosco Dataset: Detection of Visual Elements in Historical Documents

Recent advances in object detection facilitated by deep learning have led to numerous solutions in a myriad of fields ranging from medical diagnosis to autonomous driving. However, historical research is yet to reap the benefits of such advances. This is generally due to the low number of large, coherent, and annotated datasets of historical documents, as well as the overwhelming focus on Optical Character Recognition to support the analysis of historical documents. In this paper, we highlight the importance of visual elements, in particular illustrations in historical documents, and offer a public multi-class historical visual element dataset based on the Sphaera corpus. Additionally, we train an image extraction model based on YOLO architecture and publish it through a publicly available web-service to detect and extract multi-class images from historical documents in an effort to bridge the gap between traditional and computational approaches in historical studies

Machine Printed Text and Handwriting Identification in Noisy Document Images

In this paper, we address the problem of the identification of text in noisy document images. We are especially focused on segmenting and identifying between handwriting and machine printed text because: 1) Handwriting in a document often indicates corrections, additions, or other supplemental information that should be treated differently from the main content and 2) the segmentation and recognition techniques requested for machine printed and handwritten text are significantly different. A novel aspect of our approach is that we treat noise as a separate class and model noise based on selected features. Trained Fisher classifiers are used to identify machine printed text and handwriting from noise and we further exploit context to refine the classification. A Markov Random Field-based (MRF) approach is used to model the geometrical structure of the printed text, handwriting, and noise to rectify misclassifications