Titlebook: Document Analysis and Recognition – ICDAR 2021 Workshops; Lausanne, Switzerlan Elisa H. Barney Smith,Umapada Pal Conference proceedings 202

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Accurate Graphic Symbol Detection in Ancient Document Digital Reproductions. The study of such symbols may potentially reveal essential insights into the social and historical context. However, finding such symbols in handwritten documents is not an easy task, requiring the knowledge and skills of expert users, i.e., paleographers. An AI-based system can be designed, highl

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Inscription Segmentation Using Synthetic Inscription Images for Text Detection at Stone Monuments. Photography is an easy way to record inscriptions; however, the light falling on the monument, the resulting shadows, and the innate texture of the stone can make the text in the photographs unclear and difficult to recognize. This paper presents a method for inferring pixel-wise text areas in a s

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Transfer Learning for Scene Text Recognition in Indian Languagesns. In this work, we investigate the power of transfer learning for all the layers of deep scene text recognition networks from English to two common Indian languages. We perform experiments on the conventional CRNN model and STAR-Net to ensure generalisability. To study the effect of change in diff

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CATALIST: CAmera TrAnsformations for Multi-LIngual Scene Text Recognitiontion masks at multiple levels, ., line, word, and character levels while training the multi-head attention model. We demonstrate that such supervision improves training performance and testing accuracy. To train CATALIST and its attention masks, we also present a synthetic data generator ALCHEMIST t

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0302-9743 and Recognition, ICDAR 2021, held in Lausanne, Switzerland, in September 2021.The total of 59 full and 12 short papers presented in this book were carefully selected from 96 contributions and divided into two volumes. Part I contains 29 full and 4 short papers that stem from the following meetings:

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Introduction: Transmedia Crime Stories,train the model in a wider range of datasets, but also to perform better after-training predictions by applying an overfitting process on the input binary image. We report qualitative an quantitative evaluations on the . and the . datasets.