On-line cursive script recognition: A user-adaptive system for word identification

We present the last version of our system Adresy dedicated to the recognition of words written on a digitizing tablet. Adresy is designed to be used with a big (given) vocabulary. In this context, it achieves a very good performance because it is able to learn automatically the writing style of any specific user, directly from a set of a few samples of words. Moreover, Adresy improves continuously its performance in a user-transparent way, thanks to a second, faster, learning process called adaptation. This paper describes the main aspects of Adresy. Moreover, the power of our system is proven through four experiments performed on a database of ten thousand handwritten words.     

LWSINet: A deep learning-based approach towards video script identification

Multimedia Tools and Applications - Videos – a high volume of texts – broadcast via different media, such as television and the internet. Since Optical Character Recognition (OCR)...     

A multiscale approach to texture-based image retrieval

This paper presents research on a robust technique for texture-based image retrieval in multimedia museum collections. The aim is to be able to use a query image patch containing a single texture to retrieve images containing an area with similar texture to that in the query. The feature extractor used to build the feature vectors is based on an improved version of the discrete wavelet frames (DWF), proposed elsewhere. In order to utilise the feature extractor on real scene image datasets, a block-oriented decomposition technique, termed the multiscale sub-image matching method, is presented. The multiscale method, together with the DWF, provide an efficient content-based retrieval technique without the need for segmentation. The algorithms are tested on a range of databases of texture images as well as on real museum image collections. Promising results are reported.

A knowledge-based approach for script recognition without training

The approach described is based on an empirical parametric model for the handwriting recognition system. The parameters are so chosen and quantized as to retain only broad shape information, ignoring writer-dependent and other variability. Concatenation of character prototypes generates archetypal reference words for recognition, and training is unnecessary. The recognition scores exceed 90%     

Efficient zone identification approach for the recognition of online handwritten Gurmukhi script

Neural Computing and Applications - In this paper, a real-time recognition approach for online handwritten Gurmukhi character combinations with matras (Gurmukhi Vowels) has been addressed....     

A texture-based method for modeling the background and detecting moving objects

This paper presents a novel and efficient texture-based method for modeling the background and detecting moving objects from a video sequence. Each pixel is modeled as a group of adaptive local binary pattern histograms that are calculated over a circular region around the pixel. The approach provides us with many advantages compared to the state-of-the-art. Experimental results clearly justify our model.     

含噪文本图像的中英文文种识别研究

从文本图像中提取局部特征来进行文字种类识别,对图像的质量要求较高,而提取文本图像的整体特征进行识别,则能够克服了这个问题.分析中、英文文本图像的纹理特征,通过Gabor滤波器分析提取全局特征,再用支持向量机的方法进行文种分类识别.实验结果表明,该方法可以很好地区分含噪文本图像中的中文和英文.     

Network-based approach to online cursive script recognition

The idea of combining the network of HMMs and the dynamic programming-based search is highly relevant to online handwriting recognition. The word model of HMM network can be systematically constructed by concatenating letter and ligature HMM's while sharing common ones. Character recognition in such a network can be defined as the task of best aligning a given input sequence to the best path in the network. One distinguishing feature of the approach is that letter segmentation is obtained simultaneously with recognition but no extra computation is required.