Ikram Moalla

Contribution to the discrimination of the medieval manuscript texts: application in the palaeography

This work presents our first contribution to the discrimination of the medieval manuscript texts in order to assist the palaeographers to date the ancient manuscripts. Our method is based on the Spatial Grey-Level Dependence (SGLD) which measures the join probability between grey levels values of pixels for each displacement. We use the Haralick features to characterise the 15 medieval text styles. The achieved discrimination results are between 50% and 81%, which is encouraging.

Image analysis for palaeography inspection

This paper presents our first contribution to the discrimination of the medieval manuscript texts in order to assist palaeographers to date the ancient manuscripts. Our method is based on spatial grey-level dependence (SGLD) which measures the join probability between grey level values of pixels for each displacement. We use the Haralick features to characterise 15 Latin medieval text styles and then to characterise 7 Arabic styles. The achieved discrimination results are between 50% and 81% for the Medieval Latin styles, and up to 100% for Arabic ones

A Comparative Study of Local Descriptors for Arabic Character Recognition on Mobile Devices

Nowadays, the number of mobile applications based on image registration and recognition is increasing. Most interesting applications include mobile translator which can read text characters in the real world and translates it into the native language instantaneously. In this context, we aim to recognize characters in natural scenes by computing significant points so called key points or features/interest points in the image. So, it will be important to compare and evaluate features descriptors in terms of matching accuracy and processing time in a particular context of natural scene images. In this paper, we were interested on comparing the efficiency of the binary features as alternatives to the traditional SIFT and SURF in matching Arabic characters descended from natural scenes. We demonstrate that the binary descriptor ORB yields not only to similar results in terms of matching characters performance that the famous SIFT but also to faster computation suitable for mobile applications.

CNN Based Transfer Learning for Scene Script Identification

Identifying scripts in natural images is an important step in document analysis. Recently, Convolutional Neural Network (CNN) has achieved great success in image classification tasks, due to its strong capacity and invariance to translation and distortions. A problem with training a new CNN is that it requires a large amount of labelled images and extensive computation resources. Transfer learning from pre-trained models proves to ease the application of CNN and even boost the performance in some circumstances. In this paper, we use transfer learning and fine-tuning in document analysis. Indeed, we deal with the scene script identification quantitatively by comparing the performances of transfer learning and learning from scratch. We evaluate two CNN architectures trained on natural images: AlexNet and VGG-16. Experimental results on several benchmark datasets namely, SIW-13, MLe2e and CVSI2015, demonstrate that our approach outperforms previous approaches and full training.

Supervised dictionary learning in BoF framework for Scene Character recognition

In recent years, growing attention has been paid to recognizing text in natural scenes images. Scene Character recognition (SCR) is an important step in automatizing the process of reading text in natural scenes.

Generalized Eigen Cooccurrence: Application to Palaeography

This paper introduces the Generalized Eigen Cooccurrence Matrix (GECM) as a new feature to describe complex structures like images of handwritings for palaeographic expertise. It measures the spatial dependency between two features in the image. It generalizes the popular grey level cooccurrence Dependencies (SGLD) which uses the luminance for the two features. 2nd order statistics generate high dimensional feature space which must be reduced to overcome the curse of dimensionality. Haralick have described several descriptors suited for SGLD matrices that cannot be used in Generalized Cooccurrence. In our case, the cooccurrence matrices are not always symmetric and the contents of each matrice are different from the SGLD. We introduce the GECM which uses the eigen decomposition of the cooccurrence matrices to reduce the number of matrices and decrease the redundancy of spatial information instead to reduce the size of each matrix. We show the effectiveness of the GECM on palaeography application and writing comparison.