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Layout Parser : A Unified Toolkit for Deep Learning Based Document Image Analysis Zejiang Shen1( ), Ruochen Zhang2, Melissa Dell3, Benjamin Charles Germain Lee4, Jacob Carlson3, and Weining Li5 1Allen Institute for AI shannons@allenai. org 2Brown University ruochen zhang@brown. edu 3Harvard University {melissadell,jaco... | layout-parser-paper.pdf |
2 Z. Shen et al. 37], layout detection [ 38,22], table detection [ 26], and scene text detection [ 4]. A generalized learning-based framework dramatically reduces the need for the manual specification of complicated rules, which is the status quo with traditional methods. DL has the potential to transform DIA pipelines ... | layout-parser-paper.pdf |
Layout Parser : A Unified Toolkit for DL-Based DIA 3 that require precision, efficiency, and robustness, as well as simple and light-weight document processing tasks focusing on efficacy and flexibility (Section 5). Layout Parser is being actively maintained, and support for more deep learning models and novel methods in tex... | layout-parser-paper.pdf |
4 Z. Shen et al. Efficient Data Annotation C u s t o m i z e d M o d e l T r a i n i n g Model Cust omization DI A Model Hub DI A Pipeline Sharing Community Platform La y out Detection Models Document Images T h e C o r e L a y o u t P a r s e r L i b r a r y OCR Module St or age & Visualization La y out Data Structur ... | layout-parser-paper.pdf |
Layout Parser : A Unified Toolkit for DL-Based DIA 5 Table 1: Current layout detection models in the Layout Parser model zoo Dataset Base Model1Large Model Notes Pub Lay Net [38] F / M M Layouts of modern scientific documents PRIm A [3] M-Layouts of scanned modern magazines and scientific reports Newspaper [17] F-Layouts ... | layout-parser-paper.pdf |
6 Z. Shen et al. Fig. 2: The relationship between the three types of layout data structures. Coordinate supports three kinds of variation; Text Block consists of the co-ordinate information and extra features like block text, types, and reading orders; a Layout object is a list of all possible layout elements, includin... | layout-parser-paper.pdf |
Layout Parser : A Unified Toolkit for DL-Based DIA 7 Coordinates are the cornerstones for storing layout information. Currently, three types of Coordinate data structures are provided in Layout Parser, shown in Figure 2. Interval and Rectangle are the most common data types and support specifying 1D or 2D regions within... | layout-parser-paper.pdf |
8 Z. Shen et al. Table 2: All operations supported by the layout elements. The same APIs are supported across different layout element classes including Coordinate types, Text Block and Layout. Operation Name Description block. pad(top, bottom, right, left) Enlarge the current block according to the input block. scale(f... | layout-parser-paper.pdf |
Layout Parser : A Unified Toolkit for DL-Based DIA 9 Fig. 3: Layout detection and OCR results visualization generated by the Layout Parser APIs. Mode I directly overlays the layout region bounding boxes and categories over the original image. Mode II recreates the original document via drawing the OCR'd texts at their c... | layout-parser-paper.pdf |
10 Z. Shen et al. Fig. 4: Illustration of (a) the original historical Japanese document with layout detection results and (b) a recreated version of the document image that achieves much better character recognition recall. The reorganization algorithm rearranges the tokens based on the their detected bounding boxes gi... | layout-parser-paper.pdf |
Layout Parser : A Unified Toolkit for DL-Based DIA 11 focuses on precision, efficiency, and robustness. The target documents may have complicated structures, and may require training multiple layout detection models to achieve the optimal accuracy. Light-weight pipelines are built for relatively simple documents, with an ... | layout-parser-paper.pdf |
12 Z. Shen et al. To decipher the complicated layout structure, two object detection models have been trained to recognize individual columns and tokens, respectively. A small training set (400 images with approxi-mately 100 annotations each) is curated via the active learning based annotation tool [ 32] in Layout Pars... | layout-parser-paper.pdf |
Layout Parser : A Unified Toolkit for DL-Based DIA 13 Fig. 6: This lightweight table detector can identify tables (outlined in red) and cells (shaded in blue) in different locations on a page. In very few cases (d), it might generate minor error predictions, e. g, failing to capture the top text line of a table. 5. 2 A l... | layout-parser-paper.pdf |
14 Z. Shen et al. 6 Conclusion Layout Parser provides a comprehensive toolkit for deep learning-based document image analysis. The off-the-shelf library is easy to install, and can be used to build flexible and accurate pipelines for processing documents with complicated structures. It also supports high-level customizat... | layout-parser-paper.pdf |
Layout Parser : A Unified Toolkit for DL-Based DIA 15 [8]Gardner, M., Grus, J., Neumann, M., Tafjord, O., Dasigi, P., Liu, N., Peters, M., Schmitz, M., Zettlemoyer, L. : Allennlp: A deep semantic natural language processing platform. ar Xiv preprint ar Xiv:1803. 07640 (2018) [9] L ukasz Garncarek, Powalski, R., Stanis l... | layout-parser-paper.pdf |
16 Z. Shen et al. [23]Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., De Vito, Z., Lin, Z., Desmaison, A., Antiga, L., Lerer, A. : Automatic differentiation in pytorch (2017) [24]Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al. : P... | layout-parser-paper.pdf |
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