Case Study: Fine Writing Style Classification Using Siamese Neural Network

Abdalhaleem, Alaa; Barakat, Berat Kurar; El-Sana, Jihad

doi:10.1109/asar.2018.8480212

Cited by 7 publications

(2 citation statements)

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“…Keglevic et al [ 9 ] proposed to use a triplet CNN to measure the similarity of two image patches. Abdalhaleem et al [ 10 ] investigated in-writer differences in manuscripts. Their methodology is built on Siamese convolutional neural networks, which are trained to recognize little differences in a person’s writing style.…”

Section: Related Workmentioning

confidence: 99%

Digital Hebrew Paleography: Script Types and Modes

et al. 2022

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Paleography is the study of ancient and medieval handwriting. It is essential for understanding, authenticating, and dating historical texts. Across many archives and libraries, many handwritten manuscripts are yet to be classified. Human experts can process a limited number of manuscripts; therefore, there is a need for an automatic tool for script type classification. In this study, we utilize a deep-learning methodology to classify medieval Hebrew manuscripts into 14 classes based on their script style and mode. Hebrew paleography recognizes six regional styles and three graphical modes of scripts. We experiment with several input image representations and network architectures to determine the appropriate ones and explore several approaches for script classification. We obtained the highest accuracy using hierarchical classification approach. At the first level, the regional style of the script is classified. Then, the patch is passed to the corresponding model at the second level to determine the graphical mode. In addition, we explore the use of soft labels to define a value we call squareness value that indicates the squareness/cursiveness of the script. We show how the graphical mode labels can be redefined using the squareness value. This redefinition increases the classification accuracy significantly. Finally, we show that the automatic classification is on-par with a human expert paleographer.

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Section: Related Workmentioning

confidence: 99%

Digital Hebrew Paleography: Script Types and Modes

et al. 2022

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“…This eliminates excessive computation, yet achieving higher accuracy alongside efficiency. The encoder consists of a Siamese CNN network that generates vectors of input images, from which their differences are determined as envisioned in several image recognition tasks [10]. Also, the captions are adequately processed and transformed into vector representation by means of embedding lookup.…”

Section: Introductionmentioning

confidence: 99%

CaptionNet: Automatic End-to-End Siamese Difference Captioning Model With Attention

et al. 2019

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Several deep learning techniques have been intensively reviewed for captioning tasks, enabling the possibility of textual understanding, and description of both simple and complex images. In advancing this knowledge, this paper proposes a multimodal end-to-end siamese difference captioning model (SDCM) to automatically generate a natural language description of differences in an image pair. The proposed supervised learning model combines several deep learning techniques in exploring the practicability of capturing, aligning, and computing the disparities between two image features, for the purpose of creating corresponding language model probability distribution. First, a deep siamese convolutional neural network is used to extract the feature vector discrepancies of an image pair, and then an attention mechanism enables the detection of salient regions of the feature vector which effectively allows a bidirectional long short-term memory decoder to generate a matching and semantically associated textual sequence. The evaluation of the model is tested on the spot-the-diff baseline dataset which consists of pairs of images and their equivalent captions. The results indicate that our proposed model demonstrates a highly competitive performance in comparison to the state of the art. INDEX TERMSDeep convolutional neural network, Siamese network, recurrent neural network, image captioning, deep learning.

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