ICFHR2016 Competition on the Classification of Medieval Handwritings in Latin Script

Abstract

This paper presents the results of the ICFHR2016 Competition on the Classification of Medieval Handwritings in Latin Script (CLaMM), jointly organized by Computer Scientists and Humanists (paleographers). This work aims at providing a rich database of European medieval manuscripts to the community on Handwriting Analysis and Recognition. At this competition, we proposed two independent classification tasks which attracted five participants with seven submitted classifiers. Those classifiers are trained on a set 2000 images with their ground truths. In the first task-Script classificationthe classifiers have been evaluated by a test set of 1000 single-type manuscripts. In the second task, a-Fuzzy Classification‖ has been carried out on a set of 2000 multiscript-type manuscripts. The results of the participants provide the first baseline evaluation up to the accuracy score of 83.9% for the task 1 and to the fuzzy weighted score of 2.96/4 for the task 2. An analysis based on the intra-class distance and matrix of confusion of each classifier is also given.

Convolutional Neural Networks for Font Classification

“…For example, the CLaMM dataset contains 12 scribal script classes defined by expert paleographers that are handwriting styles that differ in character allographs and morphological shape [10]. However, we find that CNNs trained on CLaMM are sensitive to how dark the text is.…”

“…We use two datasets in this work, Classification of Latin Medieval Manuscripts (CLaMM) [10] and the King Fahd University Arabic Font Database (KAFD). Example training patches from each dataset are shown in Figure 1.…”

“…The CLaMM dataset was introduced in the recent ICFHR2016 Competition on the Classification of Medieval Handwritings in Latin Script [10]. It is comprised of 2000 training images and 1000 test images (for task 1) in grayscale format.…”

Deep Generalized Max Pooling

“…We mainly focus on two specific tasks: Script type or font classification and writer identification/retrieval. Script type classification is an important aspect of paleographic research [17], [18], and has recently gained more attention through the ICFHR'16 and ICDAR'17 competitions in the classification of Latin medieval manuscripts (CLaMM) [19], [20]. Tensmeyer et al [1] proposed the use of an ensemble of CNNs trained with different network architectures (for CLAMM16) and image scales (for CLAMM17).…”

“…2) CLAMM16 and CLAMM17: were introduced in the competitions on Classification of Medieval Latin Manuscripts (CLaMM) of 2016 [19] and 2017 [20], respectively. The task is image classification and both datasets contain twelve classes representing script types.…”