How to separate between Machine-Printed/Handwritten and Arabic/Latin Words?

This paper gathers some contributions to script and its nature identification. Different sets of features have been employed successfully for discriminating between handwritten and machine-printed Arabic and Latin scripts. They include some well established features, previously used in the literature, and new structural features which are intrinsic to Arabic and Latin scripts. The performance of such features is studied towards this paper. We also compared the performance of five classifiers: Bayes (AODEsr), k-Nearest Neighbor (k-NN), Decision Tree (J48), Support Vector Machine (SVM) and Multilayer perceptron (MLP) used to identify the script at word level. These classifiers have been chosen enough different to test the feature contributions. Experiments have been conducted with handwritten and machine-printed words, covering a wide range of fonts. Experimental results show the capability of the proposed features to capture differences between scripts and the effectiveness of the three classifiers. An average identification precision and recall rates of 98.72% was achieved, using a set of 58 features and AODEsr classifier, which is slightly better than those reported in similar works

A System for an automatic reading of student information sheets

ISBN: 978-1-4577-1350-7International audienceIn this paper we present a student information sheet reading system. Relevant algorithm is proposed to locate and label handwritten answer field. As information sheets can be filled in Arabic and/or in French, automating the script language differentiation is a pre-recognition required in the proposed system. We have developed a robust and fast field classification and script language identification method, based on a decision tree, to make these processing practical for sheet recognition. To this end, the system uses several novel features (loops, descenders, diacritics) and analyses the lower profile of script. The classification rates are 92.5% for numeric fields, 94.34% for Arabic scripts and 94.66% for French scripts. Experimental results, carried on 80 sheets, show our system provides an effective way to convert printed sheets into computerized format or collect information for database from printed sheets

Arabic/Latin and Machine-printed/Handwritten Word Discrimination using HOG-based Shape Descriptor

In this paper, we present an approach for Arabic and Latin script and its type identification based onHistogram of Oriented Gradients (HOG) descriptors. HOGs are first applied at word level based on writingorientation analysis. Then, they are extended to word image partitions to capture fine and discriminativedetails. Pyramid HOG are also used to study their effects on different observation levels of the image.Finally, co-occurrence matrices of HOG are performed to consider spatial information between pairs ofpixels which is not taken into account in basic HOG. A genetic algorithm is applied to select the potentialinformative features combinations which maximizes the classification accuracy. The output is a relativelyshort descriptor that provides an effective input to a Bayes-based classifier. Experimental results on a set ofwords, extracted from standard databases, show that our identification system is robust and provides goodword script and type identification: 99.07% of words are correctly classified

Arabic/Latin and Machine-printed/Handwritten Word Discrimination using HOG-based Shape Descriptor

In this paper, we present an approach for Arabic and Latin script and its type identification based onHistogram of Oriented Gradients (HOG) descriptors. HOGs are first applied at word level based on writingorientation analysis. Then, they are extended to word image partitions to capture fine and discriminativedetails. Pyramid HOG are also used to study their effects on different observation levels of the image.Finally, co-occurrence matrices of HOG are performed to consider spatial information between pairs ofpixels which is not taken into account in basic HOG. A genetic algorithm is applied to select the potentialinformative features combinations which maximizes the classification accuracy. The output is a relativelyshort descriptor that provides an effective input to a Bayes-based classifier. Experimental results on a set ofwords, extracted from standard databases, show that our identification system is robust and provides goodword script and type identification: 99.07% of words are correctly classified

Arabic/Latin and Machine-printed/Handwritten Word Discrimination using HOG-based Shape Descriptor

International audienceIn this paper, we present an approach for Arabic and Latin script and its type identification based onHistogram of Oriented Gradients (HOG) descriptors. HOGs are first applied at word level based on writingorientation analysis. Then, they are extended to word image partitions to capture fine and discriminativedetails. Pyramid HOG are also used to study their effects on different observation levels of the image.Finally, co-occurrence matrices of HOG are performed to consider spatial information between pairs ofpixels which is not taken into account in basic HOG. A genetic algorithm is applied to select the potentialinformative features combinations which maximizes the classification accuracy. The output is a relativelyshort descriptor that provides an effective input to a Bayes-based classifier. Experimental results on a set ofwords, extracted from standard databases, show that our identification system is robust and provides goodword script and type identification: 99.07% of words are correctly classified

Proposition to distinguish Machine-Printed from Handwritten Arabic and Latin Words

International audience—In this work, we gathered some contributions to identify script and its nature. We successfully employed many features to distinguish between handwritten and machine-printed Arabic and Latin scripts at word level. Some of them are previously used in the literature, and the others are here proposed. The new proposed structural features are intrinsic to Arabic and Latin scripts. The performance of all extracted features is studied towards this paper. We also compared the performance of three classifiers: Bayes (AODEsr), k-Nearest Neighbor (k-NN) and Decision Tree (J48), used to identify the script at word level. These classifiers have been chosen enough different to test the feature contributions. We carried experiments using standard databases. Obtained results demonstrate used feature capability to capture differences between scripts. Using a set of 58 selected features and a Bayes-based classifier, we achieved an average identification rate equals to 98.72%, which considered a very satisfactory rate compared to some related works

How to separate between Machine-Printed/Handwritten and Arabic/Latin Words?

This paper gathers some contributions to script and its nature identification. Different sets of features have been employed successfully for discriminating between handwritten and machine-printed Arabic and Latin scripts. They include some well established features, previously used in the literature, and new structural features which are intrinsic to Arabic and Latin scripts. The performance of such features is studied towards this paper. We also compared the performance of five classifiers: Bayes (AODEsr), k-Nearest Neighbor (k-NN), Decision Tree (J48), Support Vector Machine (SVM) and Multilayer perceptron (MLP) used to identify the script at word level. These classifiers have been chosen enough different to test the feature contributions. Experiments have been conducted with handwritten and machine-printed words, covering a wide range of fonts. Experimental results show the capability of the proposed features to capture differences between scripts and the effectiveness of the three classifiers. An average identification precision and recall rates of 98.72% was achieved, using a set of 58 features and AODEsr classifier, which is slightly better than those reported in similar works