Writer Identification Based on Arabic Handwriting Recognition by using Speed Up Robust Feature and K- Nearest Neighbor Classification

In a writer recognition system, the system performs a “one-to-many” search in a large database with handwriting samples of known authors and returns a possible candidate list. This paper proposes method for writer identification handwritten Arabic word without segmentation to sub letters based on feature extraction speed up robust feature transform (SURF) and K nearest neighbor classification (KNN) to enhance the writer's  identification accuracy. After feature extraction, it can be cluster by K-means algorithm to standardize the number of features. The feature extraction and feature clustering called to gather Bag of Word (BOW); it converts arbitrary number of image feature to uniform length feature vector. The proposed method experimented using (IFN/ENIT) database. The recognition rate of experiment result is (96.666)

Handwritten Character Recognition Based on the Specificity and the Singularity of the Arabic Language

A good Arabic handwritten recognition system must consider the characteristics of Arabic letters which can be explicit such as the presence of diacritics or implicit such as the baseline information (a virtual line on which cursive text are aligned and/join). In order to find an adequate method of features extraction, we have taken into consideration the nature of the Arabic characters. The paper investigate two methods based on two different visions: one describes the image in terms of the distribution of pixels, and the other describes it in terms of local patterns. Spatial Distribution of Pixels (SDP) is used according to the first vision; whereas Local Binary Patterns (LBP) are used for the second one. Tested on the Arabic portion of the Isolated Farsi Handwritten Character Database (IFHCDB) and using neural networks as a classifier, SDP achieve a recognition rate around 94% while LBP achieve a recognition rate of about 96%

Un nuevo descriptor para la identificación de personas mediante caracteres simples

En este artículo se presenta un nuevo descriptor para la verificación de la identidad de personas en base al análisis de imágenes en escala de grises de caracteres manuscritos individuales y simples. El descriptor corresponde a los coeficientes B-Spline de la curva de posición relativa de los puntos de mínimo valor de gris dentro del carácter. Estos puntos corresponden a los pixeles de menor valor de gris sobre la línea recta perpendicular a los puntos del esqueleto morfológico del trazo. La posición relativa se computa como la distancia euclidia entre el punto de mínimo gris y su correspondiente en el esqueleto morfológico del trazo. Se utilizó un clasificador multiclase, basado en Máquinas de Vectores Soporte de salida binaria, para evaluar la capacidad de discriminación del descriptor propuesto. Se utilizó una base de datos con 50 muestras de 6 símbolos simples realizadas por 50 personas. La experimentación de la base de datos muestra resultados muy satisfactorios, con un promedio de aciertos del 97 %, y permiten pensar que es factible desarrollar un método de identificación de personas en base al descriptor presentado.XIV Workshop Computación Gráfica, Imágenes y Visualización (WCGIV).Red de Universidades con Carreras en Informática (RedUNCI

Un nuevo descriptor para la identificación de personas mediante caracteres simples

En este artículo se presenta un nuevo descriptor para la verificación de la identidad de personas en base al análisis de imágenes en escala de grises de caracteres manuscritos individuales y simples. El descriptor corresponde a los coeficientes B-Spline de la curva de posición relativa de los puntos de mínimo valor de gris dentro del carácter. Estos puntos corresponden a los pixeles de menor valor de gris sobre la línea recta perpendicular a los puntos del esqueleto morfológico del trazo. La posición relativa se computa como la distancia euclidia entre el punto de mínimo gris y su correspondiente en el esqueleto morfológico del trazo. Se utilizó un clasificador multiclase, basado en Máquinas de Vectores Soporte de salida binaria, para evaluar la capacidad de discriminación del descriptor propuesto. Se utilizó una base de datos con 50 muestras de 6 símbolos simples realizadas por 50 personas. La experimentación de la base de datos muestra resultados muy satisfactorios, con un promedio de aciertos del 97 %, y permiten pensar que es factible desarrollar un método de identificación de personas en base al descriptor presentado.XIV Workshop Computación Gráfica, Imágenes y Visualización (WCGIV).Red de Universidades con Carreras en Informática (RedUNCI

Writer identification approach based on bag of words with OBI features

Handwriter identification aims to simplify the task of forensic experts by providing them with semi-automated tools in order to enable them to narrow down the search to determine the final identification of an unknown handwritten sample. An identification algorithm aims to produce a list of predicted writers of the unknown handwritten sample ranked in terms of confidence measure metrics for use by the forensic expert will make the final decision. Most existing handwriter identification systems use either statistical or model-based approaches. To further improve the performances this paper proposes to deploy a combination of both approaches using Oriented Basic Image features and the concept of graphemes codebook. To reduce the resulting high dimensionality of the feature vector a Kernel Principal Component Analysis has been used. To gauge the effectiveness of the proposed method a performance analysis, using IAM dataset for English handwriting and ICFHR 2012 dataset for Arabic handwriting, has been carried out. The results obtained achieved an accuracy of 96% thus demonstrating its superiority when compared against similar techniques

Dissimilarity Gaussian Mixture Models for Efficient Offline Handwritten Text-Independent Identification using SIFT and RootSIFT Descriptors

Handwriting biometrics is the science of identifying the behavioural aspect of an individual’s writing style and exploiting it to develop automated writer identification and verification systems. This paper presents an efficient handwriting identification system which combines Scale Invariant Feature Transform (SIFT) and RootSIFT descriptors in a set of Gaussian mixture models (GMM). In particular, a new concept of similarity and dissimilarity Gaussian mixture models (SGMM and DGMM) is introduced. While a SGMM is constructed for every writer to describe the intra-class similarity that is exhibited between the handwritten texts of the same writer, a DGMM represents the contrast or dissimilarity that exists between the writer’s style on one hand and other different handwriting styles on the other hand. Furthermore, because the handwritten text is described by a number of key point descriptors where each descriptor generates a SGMM/DGMM score, a new weighted histogram method is proposed to derive the intermediate prediction score for each writer’s GMM. The idea of weighted histogram exploits the fact that handwritings from the same writer should exhibit more similar textual patterns than dissimilar ones, hence, by penalizing the bad scores with a cost function, the identification rate can be significantly enhanced. Our proposed system has been extensively assessed using six different public datasets (including three English, two Arabic and one hybrid language) and the results have shown the superiority of the proposed system over state-of-the-art techniques

Automatic handwriter identification using advanced machine learning

Handwriter identification a challenging problem especially for forensic investigation. This topic has received significant attention from the research community and several handwriter identification systems were developed for various applications including forensic science, document analysis and investigation of the historical documents. This work is part of an investigation to develop new tools and methods for Arabic palaeography, which is is the study of handwritten material, particularly ancient manuscripts with missing writers, dates, and/or places. In particular, the main aim of this research project is to investigate and develop new techniques and algorithms for the classification and analysis of ancient handwritten documents to support palaeographic studies. Three contributions were proposed in this research. The first is concerned with the development of a text line extraction algorithm on colour and greyscale historical manuscripts. The idea uses a modified bilateral filtering approach to adaptively smooth the images while still preserving the edges through a nonlinear combination of neighboring image values. The proposed algorithm aims to compute a median and a separating seam and has been validated to deal with both greyscale and colour historical documents using different datasets. The results obtained suggest that our proposed technique yields attractive results when compared against a few similar algorithms. The second contribution proposes to deploy a combination of Oriented Basic Image features and the concept of graphemes codebook in order to improve the recognition performances. The proposed algorithm is capable to effectively extract the most distinguishing handwriter’s patterns. The idea consists of judiciously combining a multiscale feature extraction with the concept of grapheme to allow for the extraction of several discriminating features such as handwriting curvature, direction, wrinkliness and various edge-based features. The technique was validated for identifying handwriters using both Arabic and English writings captured as scanned images using the IAM dataset for English handwriting and ICFHR 2012 dataset for Arabic handwriting. The results obtained clearly demonstrate the effectiveness of the proposed method when compared against some similar techniques. The third contribution is concerned with an offline handwriter identification approach based on the convolutional neural network technology. At the first stage, the Alex-Net architecture was employed to learn image features (handwritten scripts) and the features obtained from the fully connected layers of the model. Then, a Support vector machine classifier is deployed to classify the writing styles of the various handwriters. In this way, the test scripts can be classified by the CNN training model for further classification. The proposed approach was evaluated based on Arabic Historical datasets; Islamic Heritage Project (IHP) and Qatar National Library (QNL). The obtained results demonstrated that the proposed model achieved superior performances when compared to some similar method