This paper presents a texture based approach for identification of writers from offline images of handwriting. Contrary to the classical texture based techniques which extract texture information at page or block level, we exploit the texture at a very small observation scale. The proposed technique divides a given handwriting into small fragments and considers each fragment as a texture. Texture descriptors including histograms of Local Binary Patterns (LBP), Local Ternary Patterns (LTP) and Local Phase Quantization (LPQ) are then computed from these fragments. The writer of a document is characterized by the set of histograms calculated from all the fragments in the writing. Two writings are compared by computing the distance between the descriptors of their writing fragments. The technique evaluated on IFN/ENIT and IAM databases comprising handwritten text in Arabic and English, respectively, realized high identification rates.

Writer identification using texture descriptors of handwritten fragments

Writer identification using curvature-free features

Hyperspectral document image processing: Applications, challenges and future prospects

Handwriting is one of the most common types of questioned writing encountered and frequently attracts the attention in litigation. Contrary to the physiological characteristics, handwriting is a behavioral characteristic thus no two individuals with mature handwriting are exactly alike or an individual cannot produce the others writing exactly. Writing behavior and individualities are examined for similarities for both specimen and questioned document, thus, it is very efficient and effective strategy for biometrics. In this paper, we present a comprehensive review of writer identification methods and intend to provide taxonomy of dataset, feature extraction methods, as well as classification (conventional and deep learning based) for writer identification. For ease of reader, we grouped the discussion into English, Arabic, Western and Other languages from script prospective, whereas, from algorithm and methods perspective, we grouped the discussion with respect to implementation steps sequence. In the end, we highlighted the challenges and open research issues in the field of writer identification. Finally, we also suggest future direction.

Writer identification using machine learning approaches: a comprehensive review

Identification of a person from his writing is one of the challenging problems; however, it is not new. No one can repudiate its applications in a number of domains, such as forensic analysis, historical documents, and ancient manuscripts. Deep learning-based approaches have proved as the best feature extractors from massive amounts of heterogeneous data and provide promising and surprising predictions of patterns as compared with traditional approaches. We apply a deep transfer convolutional neural network (CNN) to identify a writer using handwriting text line images in English and Arabic languages. We evaluate different freeze layers of CNN (Conv3, Conv4, Conv5, Fc6, Fc7, and fusion of Fc6 and Fc7) affecting the identification rate of the writer. In this paper, transfer learning is applied as a pioneer study using ImageNet (base data-set) and QUWI data-set (target data-set). To decrease the chance of over-fitting, data augmentation techniques are applied like contours, negatives, and sharpness using text-line images of target data-set. The sliding window approach is used to make patches as an input unit to the CNN model. The AlexNet architecture is employed to extract discriminating visual features from multiple representations of image patches generated by enhanced pre-processing techniques. The extracted features from patches are then fed to a support vector machine classifier. We realized the highest accuracy using freeze Conv5 layer up to 92.78% on English, 92.20% on Arabic, and 88.11% on the combination of Arabic and English, respectively.

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Automatic Visual Features for Writer Identification: A Deep Learning Approach

Writer identification from offline images of handwriting is an interesting pattern classification problem that has been investigated for many decades now. Despite significant research endeavors, the problem still remains challenging due to high intra-class variations and, at times, high similarity between writings of two individuals. This paper presents a writer identification system that relies on extraction of key points from handwriting and feeding small patches around these key points to a convolutional neural network for feature learning and classification. More specifically, we employ FAST key points and Harris corner detector to identify points of interest in the handwriting. A deep CNN is trained using small patches centered around these key points. Classification is carried out in an end-to-end manner as well as by encoding features extracted from one of the convolutional layers. Experimental study is carried out on three different datasets namely IAM, QUWI and IFN/ENIT and high identification rates are reported for each of these datasets.

Writer Identification using Deep Learning with FAST Keypoints and Harris corner detector

This paper presents a novel approach for off-line text-independent writer identification using Arabic handwritten images. Exploiting the idea that graphical fragments of handwriting characterize the writer, we propose a local approach based on texture analysis of small writing fragments where each fragment is represented by its Local Binary Pattern (LBP) histogram. The proposed method benefits from the efficiency of the LBP as a texture descriptor and the high discriminative power of handwritten fragments to improve the performance of writer identification. The proposed technique evaluated on a database of 130 writers realizes promising identification rates with reduced execution time.

Arabic Writer Identification Using Local Binary Patterns (LBP) of Handwritten Fragments

This paper 1 presents an enhanced approach for writer identification from offline Arabic handwriting samples in text-independent mode. Based on the hypothesis that graphical fragments in handwriting are individual, we propose a technique based on texture analysis where the handwriting is divided into small fragments and each fragment is represented by the histogram of oriented gradients (HOG). The set of HOG descriptors for all the fragments in the writing is used to characterize its writer. The proposed system is evaluated using writing samples of the IFN/ENIT database realizing an identification rate of 86.62% on 411 writers.

Mohamed El Youssfi El Kettani

Papers

Writer identification using texture descriptors of handwritten fragments

Writer Identification using Deep Learning with FAST Keypoints and Harris corner detector

Arabic Writer Identification Using Local Binary Patterns (LBP) of Handwritten Fragments

Arabic Writer Identification System Using the Histogram of Oriented Gradients (HOG) of Handwritten Fragments

Impact of the CNN Patch Size in the Writer Identification.