7 research outputs found
Dissimilarity Gaussian Mixture Models for Efficient Offline Handwritten Text-Independent Identification using SIFT and RootSIFT Descriptors
Handwriting biometrics is the science of identifying the behavioural aspect of an individual’s writing style and exploiting it to develop automated writer identification and verification systems. This paper presents an efficient handwriting identification system which combines Scale Invariant Feature Transform (SIFT) and RootSIFT descriptors in a set of Gaussian mixture models (GMM). In particular, a new concept of similarity and dissimilarity Gaussian mixture models (SGMM and DGMM) is introduced. While a SGMM is constructed for every writer to describe the intra-class similarity that is exhibited between the handwritten texts of the same writer, a DGMM represents the contrast or dissimilarity that exists between the writer’s style on one hand and other different handwriting styles on the other hand. Furthermore, because the handwritten text is described by a number of key point descriptors where each descriptor generates a SGMM/DGMM score, a new weighted histogram method is proposed to derive the intermediate prediction score for each writer’s GMM. The idea of weighted histogram exploits the fact that handwritings from the same writer should exhibit more similar textual patterns than dissimilar ones, hence, by penalizing the bad scores with a cost function, the identification rate can be significantly enhanced. Our proposed system has been extensively assessed using six different public datasets (including three English, two Arabic and one hybrid language) and the results have shown the superiority of the proposed system over state-of-the-art techniques
GR-RNN:Global-Context Residual Recurrent Neural Networks for Writer Identification
This paper presents an end-to-end neural network system to identify writers
through handwritten word images, which jointly integrates global-context
information and a sequence of local fragment-based features. The global-context
information is extracted from the tail of the neural network by a global
average pooling step. The sequence of local and fragment-based features is
extracted from a low-level deep feature map which contains subtle information
about the handwriting style. The spatial relationship between the sequence of
fragments is modeled by the recurrent neural network (RNN) to strengthen the
discriminative ability of the local fragment features. We leverage the
complementary information between the global-context and local fragments,
resulting in the proposed global-context residual recurrent neural network
(GR-RNN) method. The proposed method is evaluated on four public data sets and
experimental results demonstrate that it can provide state-of-the-art
performance. In addition, the neural networks trained on gray-scale images
provide better results than neural networks trained on binarized and contour
images, indicating that texture information plays an important role for writer
identification.
The source code will be available:
\url{https://github.com/shengfly/writer-identification}.Comment: To appear: Pattern Recognitio
Offline Text-Independent Writer Identification based on word level data
This paper proposes a novel scheme to identify the authorship of a document
based on handwritten input word images of an individual. Our approach is
text-independent and does not place any restrictions on the size of the input
word images under consideration. To begin with, we employ the SIFT algorithm to
extract multiple key points at various levels of abstraction (comprising
allograph, character, or combination of characters). These key points are then
passed through a trained CNN network to generate feature maps corresponding to
a convolution layer. However, owing to the scale corresponding to the SIFT key
points, the size of a generated feature map may differ. As an alleviation to
this issue, the histogram of gradients is applied on the feature map to produce
a fixed representation. Typically, in a CNN, the number of filters of each
convolution block increase depending on the depth of the network. Thus,
extracting histogram features for each of the convolution feature map increase
the dimension as well as the computational load. To address this aspect, we use
an entropy-based method to learn the weights of the feature maps of a
particular CNN layer during the training phase of our algorithm. The efficacy
of our proposed system has been demonstrated on two publicly available
databases namely CVL and IAM. We empirically show that the results obtained are
promising when compared with previous works