Currently, deep learning approaches have proven successful in the areas of handwriting recognition. Despite this, research in this field is still needed, especially in the context of multilingual online handwriting recognition scripts by adopting new network architectures and combining relevant parametric models. In this paper, we propose a multi-stage deep learning-based algorithm for multilingual online handwriting recognition based on hybrid deep Bidirectional Long Short Term Memory (DBLSTM) and SVM networks. The main contributions of our work lie in partly in the composition of a new multi-stage architecture of deep learning networks associated with effective feature vectors that integrate dynamic and visual characteristics. First, the proposed system proceeds by pretreating the acquired script and delimiting its Segments of Online Handwriting Trajectories (SOHTs). Second, two types of feature vectors combining BetaElliptic Model (BEM) and Convolutional Neural Network (CNN) are extracted for each SOHT in order to fuzzy classify them into k sub-groups using DBLSTM neural networks for both online and offline branches trained using an unsupervised fuzzy k-means algorithm. Finally, we combine the trained models to strengthen the discrimination power of the global system using SVM engine. Extensive experiments on three data sets were conducted to validate the performance of the proposed method. The experimental results show the effectiveness and complementarities of the individual modules and the advantage of their fusion.

For several decades, the offline handwriting recognition problem has escaped a satisfactory solution. In the field of online recognition, researchers have had more successful performance, but the ability to extract dynamic information from static images has not been well explored yet. In this paper, we introduce a novel multi-lingual word handwriting recovery framework based on a convolutional denoising autoencoder with an attention model for pen up / down, velocity and temporal order recovery. The proposed framework consists of extracting robust features from a handwriting image using a stacked denoising autoencoder and an encoder Bidirectional Gated Recurrent Unit (BGRU) model. Then, the obtained vectors are decoded to produce an online script with dynamic characteristics using a BGRU with temporal attention. Evaluation is done on a Latin and Arabic Online and offline handwriting character / word databases and the proposed framework achieves high competitive results. To the best of our knowledge, this is the first work of its kind.

Online signals are rich in dynamic features such as trajectory chronology, velocity, pressure and pen up/down movements. Their offline counterparts consist of a set of pixels. Thus, online handwriting recognition accuracy is generally better than offline. In this paper, we propose an original framework for recovering temporal order and pen velocity from offline multi-lingual handwriting. Our framework is based on an integrated sequence-to-sequence attention model. The proposed system involves extracting a hidden representation from an image using a Convolutional Neural Network (CNN) and a Bidirectional Gated Recurrent Unit (BGRU), and decoding the encoded vectors to generate dynamic information using a BGRU with temporal attention. We validate our framework using an online recognition system applied to a benchmark Latin, Arabic and Indian On/Off dual-handwriting character database. The performance of the proposed multi-lingual system is demonstrated through a low error rate of point coordinates and high accuracy system rate.