Post-OCR Paragraph Recognition by Graph Convolutional Networks

Paragraphs are an important class of document entities. We propose a new approach for paragraph identification by spatial graph convolutional neural networks (GCN) applied on OCR text boxes. Two steps, namely line splitting and line clustering, are performed to extract paragraphs from the lines in OCR results. Each step uses a beta-skeleton graph constructed from bounding boxes, where the graph edges provide efficient support for graph convolution operations. With only pure layout input features, the GCN model size is 3~4 orders of magnitude smaller compared to R-CNN based models, while achieving comparable or better accuracies on PubLayNet and other datasets. Furthermore, the GCN models show good generalization from synthetic training data to real-world images, and good adaptivity for variable document styles

"In vivo cryotechnique" for paradigm shift to "living morphology" of animal organs

The morphological study has been one of the major approaches in medical and biological fields. For the last century, the conventional chemical fixation and alcohol dehydration were commonly used as an easy preparation method, but it was frequently pointed out that they usually yield many structural artifacts during their preparation processes. Although both conventional quick-freezing and high-pressure freezing methods, by which animal tissues are resected and frozen for physical fixation,can reduce such structural artifacts, the tissues have to be removed from living animal organs for the freezing. Therefore, such specimens are inevitably exposed to noxious stresses of anoxia and ischemia, exhibiting only dead morphological states of animal tissues without blood circulation. To the contrary, our "in vivo cryotechnique", by which all cells and tissues in animal bodies are cryofixed in vivo, can prevent such artifacts of resected specimens. By means of the cryotechnique, it is now possible to reveal the in vivo morphology of cells and tissues in living animal organs. Actually, it has been already applied to several animal organs, such as kidney, liver, intestine, cerebellum, eye ball, blood vessel, and joint cartilage, and brought new morphological findings, reflecting their physiological significance, which had been difficult to demonstrate by the conventional preparation methods. Moreover, its application to immunohistochemistry has also revealed more precise immunolocalizations of dynamically changing molecules in living animal organs, easily translocated by ischemic stresses and anoxia caused during the tissue resection. The "in vivo cryotechnique" allows us to perform novel morphological investigations of "living" morphological states, and develops new medical and biological fields with "living morphology" during this 21st century.Biomedical Reviews 2004; 15: 1-19

Hierarchical Text Spotter for Joint Text Spotting and Layout Analysis

We propose Hierarchical Text Spotter (HTS), a novel method for the joint task of word-level text spotting and geometric layout analysis. HTS can recognize text in an image and identify its 4-level hierarchical structure: characters, words, lines, and paragraphs. The proposed HTS is characterized by two novel components: (1) a Unified-Detector-Polygon (UDP) that produces Bezier Curve polygons of text lines and an affinity matrix for paragraph grouping between detected lines; (2) a Line-to-Character-to-Word (L2C2W) recognizer that splits lines into characters and further merges them back into words. HTS achieves state-of-the-art results on multiple word-level text spotting benchmark datasets as well as geometric layout analysis tasks.Comment: Accepted to WACV 202

Towards End-to-End Unified Scene Text Detection and Layout Analysis

Scene text detection and document layout analysis have long been treated as two separate tasks in different image domains. In this paper, we bring them together and introduce the task of unified scene text detection and layout analysis. The first hierarchical scene text dataset is introduced to enable this novel research task. We also propose a novel method that is able to simultaneously detect scene text and form text clusters in a unified way. Comprehensive experiments show that our unified model achieves better performance than multiple well-designed baseline methods. Additionally, this model achieves state-of-the-art results on multiple scene text detection datasets without the need of complex post-processing. Dataset and code: https://github.com/google-research-datasets/hiertext.Comment: To appear at CVPR 202