Multilingual Scene Character Recognition System using Sparse Auto-Encoder for Efficient Local Features Representation in Bag of Features

The recognition of texts existing in camera-captured images has become an important issue for a great deal of research during the past few decades. This give birth to Scene Character Recognition (SCR) which is an important step in scene text recognition pipeline. In this paper, we extended the Bag of Features (BoF)-based model using deep learning for representing features for accurate SCR of different languages. In the features coding step, a deep Sparse Auto-encoder (SAE)-based strategy was applied to enhance the representative and discriminative abilities of image features. This deep learning architecture provides more efficient features representation and therefore a better recognition accuracy. Our system was evaluated extensively on all the scene character datasets of five different languages. The experimental results proved the efficiency of our system for a multilingual SCR.

Assisted Video Sequences Indexing : Motion Analysis Based on Interest Points

This work deals with content-based video indexing. Our viewpoint is semi-automatic analysis of compressed video. We consider the possible applications of motion analysis and moving object detection : assisting moving object indexing, summarising videos, and allowing image and motion queries. We propose an approach based on interest points. As first results, we test and compare the stability of different types of interest point detectors in compressed sequences.