A new approach for centerline extraction in handwritten strokes: an application to the constitution of a code book

International audienceWe present in this paper a new method of analysis and decomposition of handwritten documents into glyphs (graphemes) and their associated code book. The different techniques that are involved in this paper are inspired by image processing methods in a large sense and mathematical models implying graph coloring. Our approaches provide firstly a rapid and detailed characterization of handwritten shapes based on dynamic tracking of the handwriting (curvature, thickness, direction, etc.) and also a very efficient analysis method for the categorization of basic shapes (graphemes). The tools that we have produced enable paleographers to study quickly and more accurately a large volume of manuscripts and to extract a large number of characteristics that are specific to an individual or an era

RECONOCIMIENTO DE CARACTERES ALFANUMÉRICOS HACIENDO USO DE MEMORIAS ASOCIATIVAS ALFA-BETA

ResumenEn la literatura se han utilizado muchos algoritmos de Inteligencia Artificial para el reconocimiento de textos en imágenes, algunos de estos métodos más utilizados son redes neuronales, las máquinas de vectores de soporte y el más común el reconocimiento de caracteres óptico por sus siglas en inglés (OCR). En este trabajo se presenta la utilización de un algoritmo mexicano para el reconocimiento de caracteres alfanuméricos llamado Memorias Asociativas Alfa-Beta programadas en el lenguaje de programación C#. Al entrenar el algoritmo con el método de validación K-Fold Cross Validation se obtuvo un índice de asertividad del 93% utilizando una base de datos de 10 patrones o imágenes por cada clase de números y letras con resolución de 100 x 200 pixeles. El método propuesto muestra una alta competitividad contra otros sistemas de reconocimiento de caracteres.Palabra(s) Clave: Caracteres, Reconocimiento, Memorias Asociativas. RECOGNITION OF ALPHANUMERIC CHARACTERS USING ASSOCIATIVE ALPHA-BETA MEMORIESAbstractIn the literature, many models of Artificial Intelligent (AI) have been used to text recognition in images. Some models more use are Artificial Neuronal Networks (ANN), support vector machine (SVM) and the most common Optical Character Recognition (OCR). This Work shows the use of a Mexican algorithm to alphanumeric character recognition called Memorias Asociativas Alfa-Beta, programming them in language C#. The algorithm was trained with K-Fold Cross Validation getting a 93% success rate. Our data base has 10 patters per every number and letter class with a resolution 100 x 200 pixels. The proposed method shows a high competitiveness against other character recognition systems.Keywords: Character, Memorias Asociativas, Recognitio

Abordagem inovadora para a segurança da informação em documentos impressos

É inevitável a impressão em papel de documentos com informações confidenciais. A partir do momento que seja impresso, o seu rastreamento é praticamente impossível. Este trabalho apresenta um sistema capaz de proteger a informação contida em documentos impressos, limitando o acesso apenas a utilizadores credenciados para tal, através da utilização de um sistema de realidade aumentada. È possível manter um rastreamento dos acessos à informação e revogar o acesso anteriormente concedido. São apresentadas duas abordagens, utilizando Reconhecimento de Caracteres e Reconhecimento de padrões. O sistema funciona através da criação de um documento cifrado e codificado que, posteriormente, atrav´es da utilização de óculos de realidade aumentada possibilita a visualização da informação por parte do utilizador

An Integrated Segmentation and Recognition Algorithm for Text in Video

视频文本行图像识别的技术难点主要来源于两个方面:1)粘连字符的切分与识别问题;2)复杂背景中字符的切分与识别问题.为了能够同时切分和识别这两种情况中的字符,提出了一种集成型的字符切分与识别算法.该集成型算法首先对文本行图像二值化,基于二值化的文本行图像的水平投影估计文本行高度.其次根据字符笔划粘连的程度,基于图像分析或字符识别对二值图像中的宽连通域进行切分.然后基于字符识别组合连通域得到候选识别结果,最后根据候选识别结果构造词图,基于语言模型从词图中选出字符识别结果.实验表明该集成型算法大大降低了粘连字符及复杂背景中字符的识别错误率.There are two difficulties to recognize the text images which are extracted from videos: 1) how to segment and recognize the merged characters; 2) how to segment and recognize the characters with complex backgrounds.To overcome the difficulties, a novel integrated segmentation and recognition method is proposed.The method first binarizes the text image and estimates the height of the text line.Second, the connected components in the binary text image, which are wider than a threshold, are segmented based on image analysis or character recognition.Third, the connected components are selected and combined to generate the character patterns based on character recognition.Last, the best character sequence is selected based on a statistical language model.Experimental results demonstrate the effectiveness of the proposed method

Document Image Analysis Techniques for Handwritten Text Segmentation, Document Image Rectification and Digital Collation

Document image analysis comprises all the algorithms and techniques that are utilized to convert an image of a document to a computer readable description. In this work we focus on three such techniques, namely (1) Handwritten text segmentation (2) Document image rectification and (3) Digital Collation. Offline handwritten text recognition is a very challenging problem. Aside from the large variation of different handwriting styles, neighboring characters within a word are usually connected, and we may need to segment a word into individual characters for accurate character recognition. Many existing methods achieve text segmentation by evaluating the local stroke geometry and imposing constraints on the size of each resulting character, such as the character width, height and aspect ratio. These constraints are well suited for printed texts, but may not hold for handwritten texts. Other methods apply holistic approach by using a set of lexicons to guide and correct the segmentation and recognition. This approach may fail when the domain lexicon is insufficient. In the first part of this work, we present a new global non-holistic method for handwritten text segmentation, which does not make any limiting assumptions on the character size and the number of characters in a word. We conduct experiments on real images of handwritten texts taken from the IAM handwriting database and compare the performance of the presented method against an existing text segmentation algorithm that uses dynamic programming and achieve significant performance improvement. Digitization of document images using OCR based systems is adversely affected if the image of the document contains distortion (warping). Often, costly and precisely calibrated special hardware such as stereo cameras, laser scanners, etc. are used to infer the 3D model of the distorted image which is used to remove the distortion. Recent methods focus on creating a 3D shape model based on 2D distortion informa- tion obtained from the document image. The performance of these methods is highly dependent on estimating an accurate 2D distortion grid. These methods often affix the 2D distortion grid lines to the text line, and as such, may suffer in the presence of unreliable textual cues due to preprocessing steps such as binarization. In the domain of printed document images, the white space between the text lines carries as much information about the 2D distortion as the text lines themselves. Based on this intuitive idea, in the second part of our work we build a 2D distortion grid from white space lines, which can be used to rectify a printed document image by a dewarping algorithm. We compare our presented method against a state-of-the-art 2D distortion grid construction method and obtain better results. We also present qualitative and quantitative evaluations for the presented method. Collation of texts and images is an indispensable but labor-intensive step in the study of print materials. It is an often used methodology by textual scholars when the manuscript of the text does not exist. Although various methods and machines have been designed to assist in this labor, it still remains an expensive and time- consuming process, often requiring travel to distant repositories for the painstaking visual examination of multiple original copies. Efforts to digitize collation have so far depended on first transcribing the texts to be compared, thus introducing into the process more labor and expense, and also more potential error. Digital collation will instead automate the first stages of collation directly from the document images of the original texts, thereby speeding the process of comparison. We describe such a novel framework for digital collation in the third part of this work and provide qualitative results

Adding feedback to improve segmentation and recognition of handwritten numerals

Thesis (S.B. and M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999.Includes bibliographical references (leaves 68-69).by Susan A. Dey.S.B.and M.Eng

Document image processing using irregular pyramid structure

Ph.DDOCTOR OF PHILOSOPH

Proceedings of the 2010 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory

On the annual Joint Workshop of the Fraunhofer IOSB and the Karlsruhe Institute of Technology (KIT), Vision and Fusion Laboratory, the students of both institutions present their latest research findings on image processing, visual inspection, pattern recognition, tracking, SLAM, information fusion, non-myopic planning, world modeling, security in surveillance, interoperability, and human-computer interaction. This book is a collection of 16 reviewed technical reports of the 2010 Joint Workshop