Deep Adaptive Learning for Writer Identification based on Single Handwritten Word Images

There are two types of information in each handwritten word image: explicit information which can be easily read or derived directly, such as lexical content or word length, and implicit attributes such as the author's identity. Whether features learned by a neural network for one task can be used for another task remains an open question. In this paper, we present a deep adaptive learning method for writer identification based on single-word images using multi-task learning. An auxiliary task is added to the training process to enforce the emergence of reusable features. Our proposed method transfers the benefits of the learned features of a convolutional neural network from an auxiliary task such as explicit content recognition to the main task of writer identification in a single procedure. Specifically, we propose a new adaptive convolutional layer to exploit the learned deep features. A multi-task neural network with one or several adaptive convolutional layers is trained end-to-end, to exploit robust generic features for a specific main task, i.e., writer identification. Three auxiliary tasks, corresponding to three explicit attributes of handwritten word images (lexical content, word length and character attributes), are evaluated. Experimental results on two benchmark datasets show that the proposed deep adaptive learning method can improve the performance of writer identification based on single-word images, compared to non-adaptive and simple linear-adaptive approaches.Comment: Under view of 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

Self-Supervised Representation Learning for Online Handwriting Text Classification

Self-supervised learning offers an efficient way of extracting rich representations from various types of unlabeled data while avoiding the cost of annotating large-scale datasets. This is achievable by designing a pretext task to form pseudo labels with respect to the modality and domain of the data. Given the evolving applications of online handwritten texts, in this study, we propose the novel Part of Stroke Masking (POSM) as a pretext task for pretraining models to extract informative representations from the online handwriting of individuals in English and Chinese languages, along with two suggested pipelines for fine-tuning the pretrained models. To evaluate the quality of the extracted representations, we use both intrinsic and extrinsic evaluation methods. The pretrained models are fine-tuned to achieve state-of-the-art results in tasks such as writer identification, gender classification, and handedness classification, also highlighting the superiority of utilizing the pretrained models over the models trained from scratch

Classification of Arabic Autograph as Genuine ‎And Forged through a Combination of New ‎Attribute Extraction Techniques

تقترح هذه الدراسة إطارا جديدا لتقنية التحقق من التوقيع العربي. وهو يستخلص بعض السمات الديناميكية للتمييز بين التوقيعات المزورة والحقيقية. لهذا الغرض، يستخدم هذا الإطار التكيف وضعية النافذة لاستخراج تفرد من الموقعين في التوقيع بخط اليد والخصائص المحددة من الموقعين. وبناء على هذا الإطار، تقسم التوقيعات العربية أولا إلى نوافذ 14 × 14؛ كل جزء واسع بما فيه الكفاية لإدخال معلومات وافية عن أنماط الموقعين وصغيرة بما فيه الكفاية للسماح بالمعالجة السريعة. ثم، تم اقتراح نوعين من الميزات على أساس تحويل جيب التمام المنفصل، تحويل المويجة المنفصلة لاستخلاص الميزات من المنطقة ذات الاهتمام. وأخيرا، يتم اختيار شجرة القرار لتصنيف التوقيعات باستخدام الميزات المذكورة كمدخلات لها. وتجرى التقييمات على التوقيعات العربية. وكانت النتائج مشجعة جدا مع معدل تحقق 99.75٪ لاختيار سلسلة من للتوقيعات المزورة والحقيقية للتوقيعات العربية التي تفوقت بشكل ملحوظ على أحدث الأعمال في هذا المجالThis study proposes a new framework for an Arabic autograph verification technique. It extracts certain dynamic attributes to distinguish between forged and genuine signatures. For this aim, this framework uses Adaptive Window Positioning to extract the uniqueness of signers in handwritten signatures and the specific characteristics of signers. Based on this framework, Arabic autograph are first divided into 14X14 windows; each fragment is wide enough to include sufficient information about signers’ styles and small enough to allow fast processing. Then, two types of fused attributes based on Discrete Cosine Transform and Discrete Wavelet Transform of region of interest have been proposed for attributes extraction. Finally, the Decision Tree is chosen to classify the autographs using the previous attributes as its input. The evaluations are carried out on the Arabic autograph. The results are very encouraging with verification rate 99.75% for sequential selection of forged and genuine autographs for Arabic autograph that significantly outperformed the most recent work in this fiel