4,836 research outputs found
Sparse Radial Sampling LBP for Writer Identification
In this paper we present the use of Sparse Radial Sampling Local Binary
Patterns, a variant of Local Binary Patterns (LBP) for text-as-texture
classification. By adapting and extending the standard LBP operator to the
particularities of text we get a generic text-as-texture classification scheme
and apply it to writer identification. In experiments on CVL and ICDAR 2013
datasets, the proposed feature-set demonstrates State-Of-the-Art (SOA)
performance. Among the SOA, the proposed method is the only one that is based
on dense extraction of a single local feature descriptor. This makes it fast
and applicable at the earliest stages in a DIA pipeline without the need for
segmentation, binarization, or extraction of multiple features.Comment: Submitted to the 13th International Conference on Document Analysis
and Recognition (ICDAR 2015
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
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
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
Offline handwritten signature identification using adaptive window positioning techniques
The paper presents to address this challenge, we have proposed the use of
Adaptive Window Positioning technique which focuses on not just the meaning of
the handwritten signature but also on the individuality of the writer. This
innovative technique divides the handwritten signature into 13 small windows of
size nxn(13x13).This size should be large enough to contain ample information
about the style of the author and small enough to ensure a good identification
performance.The process was tested with a GPDS data set containing 4870
signature samples from 90 different writers by comparing the robust features of
the test signature with that of the user signature using an appropriate
classifier. Experimental results reveal that adaptive window positioning
technique proved to be the efficient and reliable method for accurate signature
feature extraction for the identification of offline handwritten signatures.The
contribution of this technique can be used to detect signatures signed under
emotional duress.Comment: 13 pages, 9 figures, 2 tables, Offline Handwritten Signature, GPDS
dataset, Verification, Identification, Adaptive window positionin
Automatic handwriter identification using advanced machine learning
Handwriter identification a challenging problem especially for forensic investigation. This topic has received significant attention from the research community and several handwriter identification systems were developed for various applications including forensic science, document analysis and investigation of the historical documents. This work is part of an investigation to develop new tools and methods for Arabic palaeography, which is is the study of handwritten material, particularly ancient manuscripts with missing writers, dates, and/or places. In particular, the main aim of this research project is to investigate and develop new techniques and algorithms for the classification and analysis of ancient handwritten documents to support palaeographic studies.
Three contributions were proposed in this research. The first is concerned with the development of a text line extraction algorithm on colour and greyscale historical manuscripts. The idea uses a modified bilateral filtering approach to adaptively smooth the images while still preserving the edges through a nonlinear combination of neighboring image values. The proposed algorithm aims to compute a median and a separating seam and has been validated to deal with both greyscale and colour historical documents using different datasets. The results obtained suggest that our proposed technique yields attractive results when compared against a few similar algorithms.
The second contribution proposes to deploy a combination of Oriented Basic Image features and the concept of graphemes codebook in order to improve the recognition performances. The proposed algorithm is capable to effectively extract the most distinguishing handwriter’s patterns. The idea consists of judiciously combining a multiscale feature extraction with the concept of grapheme to allow for the extraction of several discriminating features such as handwriting curvature, direction, wrinkliness and various edge-based features. The technique was validated for identifying handwriters using both Arabic and English writings captured as scanned images using the IAM dataset for English handwriting and ICFHR 2012 dataset for Arabic handwriting. The results obtained clearly demonstrate the effectiveness of the proposed method when compared against some similar techniques.
The third contribution is concerned with an offline handwriter identification approach based on the convolutional neural network technology. At the first stage, the Alex-Net architecture was employed to learn image features (handwritten scripts) and the features obtained from the fully connected layers of the model. Then, a Support vector machine classifier is deployed to classify the writing styles of the various handwriters. In this way, the test scripts can be classified by the CNN training model for further classification. The proposed approach was evaluated based on Arabic Historical datasets; Islamic Heritage Project (IHP) and Qatar National Library (QNL). The obtained results demonstrated that the proposed model achieved superior performances when compared to some similar method
- …