Abstract: The present application relates to a method performed by an electronic device for positioning a table in a PDF document. The method comprises the following steps: receiving the PDF document containing the table; extracting character information and line information from vector stream information of the PDF document; and positioning a table area in the PDF document according to the extracted character information and line information. The method and the device in the present application perform table area positioning based on all lines and text blocks in a page. Compared with the prior art, the method and the device have the advantages that the accuracy of the table area positioning can be greatly improved, and a foundation is provided for accurate analysis of table information.

Abstract: The invention relates to an image preprocessing method and device for a JPEG compressed file. The method comprises the following steps: receiving a JPEG image to be processed; and de-noising the received JPEG image by using a pre-trained full convolutional network model to obtain a de-noised and resolution-improved image. The method and the device in the invention can effectively improve the definition of an image in an electronic file, thus effectively facilitate the subsequent image-based file analysis, e.g., OCR and CHART parsing.

Abstract: The present application relates to a method and a device for parsing a table in a document image. The method comprises the following steps: inputting a document image to be parsed which includes one or more table areas into the electronic device; detecting, by the electronic device, a table area in the document image by using a pre-trained table detection model; detecting, by the electronic device, internal text blocks included in the table area by using a pre-trained text detection model; determining, by the electronic device, a space structure of the table; and performing text recognition on a text block in each cell according to the space structure of the table, so as to obtain editable structured data by parsing. The method and the device of the present application can be applied to various tables such as line-including tables or line-excluding tables or black-and-white tables.

Abstract: The present application relates to a method and device for extracting information from a histogram for display on an electronic device. The method comprises the following steps: inputting, into the electronic device, a document, which includes a histogram to be processed; detecting each element in the histogram to be processed by using a target detection method based on a Faster R-CNN model pre-stored in the electronic device; performing text recognition on each detected text element box by the electronic device; to extract corresponding text information; and converting all the detected elements and text information into structured data for display on the electronic device. The method and the device can detect all the elements in the histogram through deep learning and the use of the Faster R-CNN model for target detection, thus providing a simple and effective solution for information extraction in the histogram.

Abstract: The present application relates to a method and a device for extracting information from a pie chart. The method comprises the following steps: detecting each element in a pie chart to be processed and position information thereof, wherein the elements comprise text elements and legend elements; performing text recognition on the detected text elements and legend elements to obtain text information corresponding to the text elements and legend texts included in the legend elements respectively; and obtaining sector information and legend information according to each detected element and position information thereof and the legend texts, and enabling the sector information to correspond to the legend information one by one, wherein the sector information comprises a sector color and a proportion of the sector in the pie chart, and the legend information comprises a legend color and a corresponding legend text thereof.

Abstract: The present application relates to an image segmentation method and device operating on an electronic device. The image segmentation method comprises the following steps: performing deep learning to obtain an FCN (Fully Convolutional Network) model, and calculating the loss using L(pji)=?(1?pji)r log(pji) in the deep learning process; inputting an image to be segmented to the last updated FCN model to obtain the probability that each pixel in the image to be segmented is in each of the categories, and selecting the category corresponding to the maximum probability as the category determined by the image segmentation for the pixel. By improving the loss function of the FCN model, the accuracy of image classification is improved, and chart information in an electronic document is accurately extracted by means of pixel classification.

Abstract: The present application relates to a method performed at an electronic device for parsing tables in a PDF document. The method includes the following steps: receiving the PDF document containing a table area; extracting horizontal lines, vertical lines and text blocks in the table area; determining the types of tables in the table area according to the extracted horizontal lines and vertical lines; if the table is a quasi full-line table, determining the structure of the quasi full-line table in the table area according to the horizontal lines and the vertical lines in the table area with the assistance of the text blocks in the table area; and if the table is a quasi non-line table, determining the structure of the quasi non-line table in the table area according to the text blocks in the table area with the assistance of the horizontal lines and/or the vertical lines in the table area.

Abstract: The present application relates to a method and device for segmenting lines in a line chart. The device converts a line chart in which N legends are detected from an RGB color model to an LAB space model to obtain an LAB value of each legend in the line chart and an LAB value of each pixel in the line chart respectively; performs Euclidean distance calculation on the LAB value of each legend and the LAB value of each pixel in the line chart to generate N color difference distribution maps, one legend corresponding to one color difference distribution map; and finds out the minimum in each color difference distribution map, setting a segmentation threshold with reference to the minimum, and segmenting a rough line corresponding to the color difference distribution map in the color difference distribution map according to the segmentation threshold.