Segmentation-free writer identification based on convolutional neural network. (July 2020) - Kumar, Parveen
Highlights: A SEGmentation-free Writer Identification (SEG-WI) model based on CNN is proposed to identify the writer. Region selection mechanism is also develop to improve the overall performance of the model. The model utilizes the convolution layers with different kernel size and stride. A new training strategy is suggested to train the model. The model has been evaluated for various databases such as IAM, CVL, IFN/ENIT, Kannada and Devnagri (Hindi) script. Graphical abstract: Abstract: Handwriting recognition is one of the desired aspects of document understanding and analysis. It deals with the writing style of the document and learns the features which differentiate the writers. In this paper, a SEGmentation-free Writer Identification (SEG-WI) model is proposed based on a convolution neural network and a weakly supervised region selection mechanism. The model, SEG-WI, takes an unsegmented text document and produces the writer-ID with the region probability map. The probability vectors at each cell location in the input document constitute a region probability map. To achieve the best performance, top 10% to 50% cell regions are selected for decision making and a voting mechanism among the selected regions is used to identify the writer. The region selection, voting mechanism for decision making, and loss calculation are the main contributions in this work, which enables the proposed system as segmentation free. The proposed model is evaluated on different datasets such as IAM handwriting database (IAM), and computer vision lab (CVL) for English, Institut of communications technology/Ecole Nationale d'Ing nieurs de Tunis (IFN/ENIT) for Arabic, Kannada, and Devanagari for Indic, and outperforms compare with the state-of-the-art results. Moreover, a comparative analysis of the proposed model with and without region selection is performed to validate the effect of the region selection mechanism and it improves the performance of the model.