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

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

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

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

Rare Case of a Well-Differentiated Paratesticular Sarcoma of the Spermatic Cord in a 60-Year-Old Patient

Introduction. Liposarcomas are tumors that occur mostly in the retroperitoneum. Of all liposarcomas only 3 to 7% are found in the paratesticular region. The spermatic cord is the main site of origin in these cases. The patients ages range from 50 to 60 years. This malignant disease can result in a loss of fertility aside from life-threatening sequelae. Case. We present a case of a liposarcoma of the paratesticular region. A 60-year-old man was referred with a painless mass in the scrotum and the right inguinal region. The patient underwent surgery and the mass was removed along with the right testis, the spermatic cord, and the soft tissues to the internal inguinal ring. Histopathological examination found a well-differentiated liposarcoma of 80⁎80 mm. The surgical margins were negative. The adjuvant treatment consisted in radiation therapy of the right inguinoscrotal area to the dose of 54 Gray, 2 Gy per session, 5 times a week. Conclusion. Paratesticular liposarcomas are rare tumors. Surgery with large margin resections was the main treatment in all reported cases. The adjuvant treatment is still unclear especially when the surgical margins are negative. The main factor that indicated this adjuvant treatment was the size of the tumor and the histologic subtype

Post-operative radiotherapy of conjunctival malignancies: A series of 24 cases

Objective: To assess the results of post-operative radiation therapy in the management of incompletely resected conjunctival malignancies. Methods: In this retrospective case series, we reviewed the clinical records of all cases of conjunctival tumors treated with post-operative radiotherapy in the radiation oncology department of Salah Azaïz Institute of Tunis, from January 1990 to December 2015. We focused on clinico-pathological characteristics, treatment modalities and patients’ outcome. Results: Twenty four patients were enrolled in our study: 19 men and 5 women. The mean age of our patients was 54 years (range: 20 to 84). The mean basal diameter of the tumor was 11 mm (range 6 to 20 mm). The mean tumor thickness was 4 mm (range 1 to 15 mm). The most frequent histological type was squamous cell carcinoma in 23 cases. One patient had a malignant conjunctival fibrohistiocytoma. Radiation therapy was post-operative for positive or narrow surgical margins in all cases. Eighteen patients were treated with kilovoltage radiation therapy (KVRT). The mean delivered dose to the tumor bed was 64 Gy (range: 60 to 70 Gy). Four patients were treated with an association of KVRT and Strontium 90 plaque brachytherapy. Two patients were treated only with Strontium 90 plaque brachytherapy (2 fractions of 17 Gy). After a median follow-up of 110 months, 19 patients were alive with no evidence of local recurrence in 17 patients. Two patients had a local recurrence and were referred to surgery. Two patients were lost to follow up. The 5-year relapse free survival rate was 90.9%. Radiation-induced side effects were conjunctivitis, cataract, eye watering and glaucoma. Conclusion: Post-operative radiation therapy allows good local control with acceptable toxicities in conjunctival malignancies. Management of these tumors needs a broad collaboration between ophthalmologists and radiation oncologists, to allow a conservative treatment with the lowest rates of local recurrence