A computer-implemented method (300) for extracting content (302) from a physical writing surface (304), the method (300) comprising the steps of: (a) receiving a reference frame (306) including image data relating to at least a portion of the physical writing surface (304), the image data including a set of data points; (b) determining an extraction region (308), the extraction region (308) including a subset of the set of data points from which content (302) is to be extracted; (c) extracting content (302) from the extraction region (308) and writing the content (302) to a display frame (394); (d) receiving a subsequent frame (406) including subsequent image data relating to at least a portion of the physical writing surface (304), the subsequent image data including a subsequent set of data points; (e) determining a subsequent extraction region (408), the subsequent extraction region (408) including a subset of the subsequent set of data points from which content (402) is to be extracted; and (f) extracting subsequent content (402) from the subsequent extraction region (408) and writing the subsequent content (402) to the display frame (394).

In the present disclosure, a candidate area is determined based on a pixel having a specific color included in an input image, and an area is determined to be a processing target from the candidate area based on a pixel having a predetermined color different from the specific color included in the candidate area. Further, a second binary image in which a pixel corresponding to the pixel having the specific color is converted into a white pixel is generated by converting, in a first binary image obtained by the input image being binarized, a pixel that is included in the area determined to be the processing target and corresponds to the pixel having the specific color, into a white pixel.

A method performed by a server, may include: obtaining an image including a first text and a second text overlapping the first text; separating a first text region corresponding to the first text from the image; extracting pixels corresponding to the first text from the first text region to obtain an undamaged portion and a damaged portion of the first text; and reconstructing the first text by inpainting the damaged portion of the first text in which the first text overlaps the second text in the image.

A first binary image is generated by binarizing an input image based on a threshold, a second binary image is generated by changing a pixel that has predetermined high luminance in the input image into a black pixel, and whether a black pixel cluster in the second binary image is made to be an extraction target is determined based on a position of a character image identified based on a black pixel cluster in the first binary image, and a position of the black pixel cluster in the second binary image.

An information processing apparatus includes a processor configured to acquire, from a read image, a predetermined item, and a value corresponding to the item, the read image being obtained by reading a document and being subjected, prior to acquisition of the item and the value, to preprocessing and character recognition. Further, the processor is configured to, in response to not successfully acquiring at least one of the item and the value, change a setting on the preprocessing or a setting on the character recognition in accordance with the acquisition or non-acquisition state of the item and the value, and then perform the preprocessing or the character recognition. In response to not successfully acquiring at least one of the item and the value, the processor is further configured to identify where the item and the value are located.

Vehicles and other items often have corresponding documentation, such as registration cards, that includes a significant amount of informative textual information that can be used in identifying the item. Traditional OCR may be unsuccessful when dealing with non-cooperative images. Accordingly, features such as dewarping, text alignment, and line identification and removal may aid in OCR of non-cooperative images. Dewarping involves determining curvature of a document depicted in an image and processing the image to dewarp the image of the document to make it more accurately conform to the ideal of a cooperative image. Text alignment involves determining an actual alignment of depicted text, even when the depicted text is not aligned with depicted visual cues. Line identification and removal involves identifying portions of the image that depict lines and removing those lines prior to OCR processing of the image.

Vehicles and other items often have corresponding documentation, such as registration cards, that includes a significant amount of informative textual information that can be used in identifying the item. Traditional OCR may be unsuccessful when dealing with non-cooperative images. Accordingly, features such as dewarping, text alignment, and line identification and removal may aid in OCR of non-cooperative images. Dewarping involves determining curvature of a document depicted in an image and processing the image to dewarp the image of the document to make it more accurately conform to the ideal of a cooperative image. Text alignment involves determining an actual alignment of depicted text, even when the depicted text is not aligned with depicted visual cues. Line identification and removal involves identifying portions of the image that depict lines and removing those lines prior to OCR processing of the image.

Provided herein are systems and methods for extracting text from a document. Different optical character recognition (OCR) tools are used to extract different versions of the text in the document. Metrics evaluating the quality of the extracted text are compared to identify and select higher quality extracted text. A selected portion of text is compared to a threshold to ensure minimal quality. The selected portion of text is then saved. Error correction can be applied to the selected portion of text based on errors specific to the OCR tools or the document contents.

A device receives historical application creation data that includes data associated with creation of a plurality of applications, and processes the historical application creation data, with one or more data processing techniques, to generate processed historical application creation data. The device trains a machine learning model, with the processed historical application creation data, to generate a trained machine learning model, and receives new application data associated with a new application to be created. The device processes the new application data, with the trained machine learning model, to generate one or more predictions associated with the new application, and performs one or more actions based on the one or more predictions associated with the new application.

Systems and techniques for masking non-public content in screen images are provided. An example system includes a screen capture tool, a region-based object detection system, a classifier, and an image masking engine. The screen capture tool may be configured to generate a screen image representing a screen being displayed by the system. The region-based object detection system may be configured to identify multiple regions within the screen image as potential non-public content regions. The classifier may be configured to selectively classify the identified regions as non-public content regions. The image masking engine may be configured to generate a masked image by masking the regions classified as non-public content regions in the screen image.