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As part of an innovative e-education project, a digital workbook is being developed to help teach handwriting at school for children aged three to seven. The main objective of this project is to offer an advanced digital writing experience at school by using pen-based tablets. In this paper, an automatic qualitative analysis process of cursive handwriting words is presented. This approach is original because the goal is not to recognise the word that was handwritten by children (it is an explicit instruction) but to design a precise evaluation of the quality of his handwriting production to give them a real-time feedback. The presented method is based on a specific explicit elastic letter spotting segmentation able to deal with the imprecision of the handwriting of young children. This approach is suited to automatically and precisely highlight the difficulties encountered by children (adding or missing letters, incorrect shapes...). The validation of the proposed approach has been done on a dataset collected in French preschools and primary schools from 231 children. Beyond quantitative results, this paper reports the very positive impact of using this digital workbook that allows children to work independently with online and real-time feedbacks.2

IntuiScript is an innovative project aiming at the development of a digital workbook providing feedback during the handwriting learning process for children from three to seven years old. In this context, the paper presents a method to analyse handwriting quality that responds to the expectations of the IntuiScript educational scenario: on-line and real time feedback for children, an automatic detection of children mistakes guiding the pedagogical progression, and a precise analysis of children writing saved to help teacher to understand children writing skills. The presented method introduces a multi-criteria architecture to analyse handwriting quality based on three different aspects: shape, order and direction. The validation of the proposed approach is done on a realistic dataset collected in preschools and primary schools with 952 children. Results show a positive feedback of children and teachers about the use of tactile digital devices, and a significant improvement of the performances of the multi-criteria architecture compared to the previous analyser. The ground truth has been annotated by experts with different levels of confidence. Specific evaluation metrics are introduced to deal with confidence annotations.

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Abstract.A novel tracklet association framework is introduced to perform robust online re-identification of pedestrians in crowded scenes recorded by a single camera. Recent advances in multi-target tracking allow the generation of longer tracks, but problems of fragmentation and identity switching remain, due to occlusions and interactions between subjects. To address these issues, a discriminative and efficient descriptor is proposed to represent a tracklet as a bag of independent motion signatures using spatio-temporal histograms of oriented gradients. Due to the significant temporal variations of these features, they are generated only at automatically identified key poses that capture the essence of its appearance and motion. As a consequence, the re-identification involves only the most appropriate features in the bag at given time. The superiority of the methodology is demonstrated on two publicly available datasets achieving accuracy over 90% of the first rank tracklet associations.

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