The impact of the image processing in the indexation system

This paper presents an efficient word spotting system applied to handwritten Arabic documents, where images are represented with bag-of-visual-SIFT descriptors and a sliding window approach is used to locate the regions that are most similar to the query by following the query-by-example paragon. First, a pre-processing step is used to produce a better representation of the most informative features. Secondly, a region-based framework is deployed to represent each local region by a bag-of-visual-SIFT descriptors. Afterward, some experiments are in order to demonstrate the codebook size influence on the efficiency of the system, by analyzing the curse of dimensionality curve. In the end, to measure the similarity score, a floating distance based on the descriptor’s number for each query is adopted. The experimental results prove the efficiency of the proposed processing steps in the word spotting system

Combined cosine-linear regression model similarity with application to handwritten word spotting

The similarity or the distance measure have been used widely to calculate the similarity or dissimilarity between vector sequences, where the document images similarity is known as the domain that dealing with image information and both similarity/distance has been an important role for matching and pattern recognition. There are several types of similarity measure, we cover in this paper the survey of various distance measures used in the images matching and we explain the limitations associated with the existing distances. Then, we introduce the concept of the floating distance which describes the variation of the threshold’s selection for each word in decision making process, based on a combination of Linear Regression and cosine distance. Experiments are carried out on a handwritten Arabic image documents of Gallica library. These experiments show that the proposed floating distance outperforms the traditional distance in word spotting system

Segmentation-free Word Spotting for Handwritten Arabic Documents

In this paper we present an unsupervised segmentation-free method for spotting and searching query, especially, for images documents in handwritten Arabic, for this, Histograms of Oriented Gradients (HOGs) are used as the feature vectors to represent the query and documents image. Then, we compress the descriptors with the product quantization method. Finally, a better representation of the query is obtained by using the Support Vector Machines (SVM)

Crack Detection and Classification in Moroccan Pavement Using Convolutional Neural Network

Crack is a condition indicator of the pavement’s structure. Generally, crack detection is an essential task for effective diagnosis of the road network. Moreover, evaluation of road quality is necessary to ensure traffic security. Since 2011, a periodic survey of approximately 57,500 km of Moroccan roads has been performed using an inspection vehicle (SMAC) which is equipped with high resolution cameras and GPS/DGPS receivers. Until recently, the teams of the National Center for Road Studies and Research (CNER) analyzed road surface states by visualization of pavement surface image sequences captured by the Multifunctional Pavement Assessment System (SMAC) in order to detect defects in road surfaces and classify them according to their type. However, this method involves manual processing and is complex, time consuming and subjective. In this paper, we propose an automated methodology for crack detection and classification in Moroccan flexible pavements using Convolutional Neural Networks (CNN). Transfer learning is also applied by testing a pre-trained Visual Geometry Group 19 (VGG-19) model. For the dataset used in this paper, the results indicate that good crack detection and classification are achieved using both models

Crack Detection and Classification in Moroccan Pavement Using Convolutional Neural Network

Crack is a condition indicator of the pavement’s structure. Generally, crack detection is an essential task for effective diagnosis of the road network. Moreover, evaluation of road quality is necessary to ensure traffic security. Since 2011, a periodic survey of approximately 57,500 km of Moroccan roads has been performed using an inspection vehicle (SMAC) which is equipped with high resolution cameras and GPS/DGPS receivers. Until recently, the teams of the National Center for Road Studies and Research (CNER) analyzed road surface states by visualization of pavement surface image sequences captured by the Multifunctional Pavement Assessment System (SMAC) in order to detect defects in road surfaces and classify them according to their type. However, this method involves manual processing and is complex, time consuming and subjective. In this paper, we propose an automated methodology for crack detection and classification in Moroccan flexible pavements using Convolutional Neural Networks (CNN). Transfer learning is also applied by testing a pre-trained Visual Geometry Group 19 (VGG-19) model. For the dataset used in this paper, the results indicate that good crack detection and classification are achieved using both models

Ultrasonic signal noise reduction based on convolutional autoencoders for NDT applications

One of the most challenging problems of ultrasonic non-destructive testing is the signal distortion caused by the presence of noise, yielding the sound wave corruption and thus degrading the ultrasonic imaging technology performance due to Time of flight methods’ loss of precision. Deep learning algorithms have proven their effectiveness in reducing noise on several types of signals in different domains. In this paper, we propose a one-dimensional convolutional autoencoder for ultrasonic signal denoising. The efficiency of the proposed architecture is compared to the wavelet decomposition method, collating the peak signal-to-noise ratio values on the denoised signals. Our method proved its potential for NDT applications in recovering temporal information even on very noisy signals, and improving the PSNR by about 30 dB

Feature Selection of Arabic Online Handwriting Using Recursive Feature Elimination for Parkinson’s Disease Diagnosis

Parkinson’s disease (PD) is one of the most common neurodegenerative diseases affecting a large population worldwide. Parkinson’s disease is characterized by rigidity, slowness of movement and tremors at rest, these syndromes are frequently manifested in the deterioration of handwriting. The aim of this article is to perform online Arabic handwriting analysis for two types of tasks, TASK 1: copying arabic imposed text and TASK 2: writing arabic desired text. A novel method of handwriting selection features is proposed to obtain the relevant features to efficiently identify subjects with PD, based on Recursive Feature Elimination with Cross-Validation (RFECV), three different RFE estimators were compared: Support Vector Machine, Decision Trees and Random Forest, the selected features have been fed to the same classifiers above to determine the best classifier for predicting Parkinson’s disease. Result: An accuracy of 94.4% was obtained using SVM with Linear kernel, based on 55 features selected using RFE-SVM(Linear) for TASK 1, for TASK 2 an accuracy of 93.7% was obtained using SVM with RBF kernel, based only in 7 features selected using RFE-SVM(Linear). For all the classifiers used, this technique experimentally demonstrates an increase in performance metrics

Segmentation-free Word Spotting for Handwritten Arabic Documents

In this paper we present an unsupervised segmentation-free method for spotting and searching query, especially, for images documents in handwritten Arabic, for this, Histograms of Oriented Gradients (HOGs) are used as the feature vectors to represent the query and documents image. Then, we compress the descriptors with the product quantization method. Finally, a better representation of the query is obtained by using the Support Vector Machines (SVM)

CHARACTERISTICS OF ARABIC HANDWRITING ON GRAPHIC TABLET IN NEURODEGENERATIVE DISEASE: PRELIMINARY RESULTS BETWEEN PATIENTS WITH ALZHEIMER’S DISEASE AND HEALTHY CONTROLS

Handwriting is a component of the complex language that came about late in the history of mankind and which develops late in human beings. Numerous works have raised changes in both the graphic and kinematic characteristics of writing. Although, age does not modify the lexical and syntactic parameters of language, it can however modify its spatial structure, especially pressure and speed. Many neurodegenerative pathologies, especially Alzheimer's disease, are characterized by a progressive disorganization of writing. Depending on the cognitive stage of the dementia, the graphic gesture deteriorates as does the spatial construction. Objective: Our study aims at assessing the characteristics of Arabic writing in a healthy Moroccan population and to compare it to people with mild to moderate Alzheimer's disease. Our objective is to help health professionals detect early cognitive deterioration in neurodegenerative diseases by analyzing the graphic gesture. Handwriting is captured on a graphic tablet (WACOM) and is analyzed "online" as a sequence of acquired signals (position, pressure, speed and pen inclination) in Moroccan patients with mild to moderate Alzheimer's disease and these were compared to those of normal volunteers. We performed a first analysis of the results from 18 Alzheimer's patients compared to 18 control subjects. The results reveal differences between the control and Alzheimer's groups. AD subjects had lower speeds and accelerations compared to the control subjects. The time spent on paper and in the air was significantly greater in the AD subjects. This preliminary analysis of the results allowed us to identify distinguishing characteristics through the analysis of different handwriting parameters in order to identify the two groups studied

Étude de l'écriture manuscrite sur tablette graphique pour l'aide au diagnostic précoce des maladies neurodégénératives: premiers résultats chez des patients parkinsoniens

International audienceIntroduction- L'écriture est un processus cognitif qui résulte de l'interaction entre plusieurs facteurs du développement cognitifs, linguistiques, et psychomoteurs. De ce fait, la détérioration de celle-ci est un signe de dysfonctionnement dans l'une des sphères cognitive