An example non-transitory machine-readable storage medium encoded with instructions executable by a processor is provided. The machine-readable storage medium includes instructions to identify fiducials on a target comprising multiple sample areas, instructions to determine positions of the fiducials in relation to the multiple sample areas, and instructions to detect a different region of interest (ROI) associated with each of the multiple sample areas based upon the positions. The machine-readable storage medium further includes instructions to calculate an optical performance metric based upon the ROI associated with each sample area, and instructions to determine a text resolution based upon the optical performance metric.

Biometric data are obtained from biometric sensors on a stand-alone computing device, which may contain an ASIC, connected to or incorporated within it. The computing device and ASIC, in combination or individually, capture biometric samples, extract biometric features and match them to one or more locally stored, encrypted templates. The biometric matching may be enhanced by the use of an entered PIN. The biometric templates and other sensitive data at rest are encrypted using hardware elements of the computing device and ASIC, and/or a PIN hash. A stored obfuscated Password is de-obfuscated and may be released to the authentication mechanism in response to successfully decrypted templates and matching biometric samples. A different de-obfuscated password may be released to authenticate the user to a remote or local computer and to encrypt data in transit. This eliminates the need for the user to remember and enter complex passwords on the device.

An information processing apparatus includes: a first extracting unit that extracts a position of a character entry box in an input image; a recognizing unit that recognizes a character string written in the character entry box; a calculating unit that calculates recognition accuracy of each of characters of the character string recognized by the recognizing unit; a first detector that detects that a value based on the recognition accuracy is equal to or larger than a preset threshold value; a second extracting unit that extracts a position of a circumscribed rectangle for each character of the character string in the input image; a second detector that detects contact of the circumscribed rectangle with the character entry box; and a display that displays the character string to be corrected on the basis of a result of detection by the first detector and a result of detection by the second detector.

A non-transitory computer-readable recording medium recording a character area extraction program for causing a computer to execute a process includes changing a relationship in relative sizes between an image and a scanning window that scans the image; scanning the scanning window based on a changed relationship, specifying a scanning position at which an edge density of an image area included in the scanning window is equal to or larger than a threshold value, extracting one or more areas indicated by the scanning window at the specified scanning position as one or more character area candidates, determining, when overlapped character area candidates included in the one or more character area candidates overlap with each other, a maximum character area candidate having a maximum edge density among the overlapped character area candidates, and extracting the image area included in the maximum character area candidate as a character area.

An example non-transitory machine-readable storage medium encoded with instructions executable by a processor is provided. The machine-readable storage medium includes instructions to identify fiducials on a target comprising multiple sample areas, instructions to determine positions of the fiducials in relation to the multiple sample areas, and instructions to detect a different region of interest (ROI) associated with each of the multiple sample areas based upon the positions. The machine-readable storage medium further includes instructions to calculate an optical performance metric based upon the ROI associated with each sample area, and instructions to determine a text resolution based upon the optical performance metric.

Systems and methods for assigning word fragments to lines of text in optical character recognition (OCR) extracted data can include at least one processor obtaining a plurality of word fragments from OCR generated data associated with an image. The at least one processor can determine vertical coordinates of each of the word fragments in the image. The at least one processor can cluster the plurality of word fragments into one or more clusters of word fragments based on the vertical coordinates of the plurality of word fragments. The at least one processor can assign each word fragment of a respective cluster to a corresponding text line based on the clustering.

Embodiments discussed herein relate to accessing a digitized image associated with a patient of tissue demonstrating breast cancer pathology; segmenting a tumor region represented in the digitized image; segmenting collagen fibers represented in the tumor region; computing collagen vectors based on the segmented collagen fibers; generating an orientation co-occurrence matrix based on the collagen vectors; computing a collagen fiber orientation disorder feature based on the co-occurrence matrix; upon determining that the collagen fiber orientation feature exceeds a threshold value: generating a prognosis of the region of tissue as unlikely to experience breast cancer recurrence; upon determining that the collagen fiber orientation feature is less than or equal to the threshold value: generating a prognosis of the region of tissue as likely to experience breast cancer recurrence; classifying the patient as high-risk of recurrence or low-risk of recurrence based, at least in part, on the prognosis; and displaying the classification.

In some embodiments, a method includes training a first machine learning model based on multiple documents and multiple templates associated with the multiple documents. The method further includes executing the first machine learning model to generate multiple relevancy masks, the multiple relevancy masks to remove a visual structure of the multiple templates from a visual structure of the multiple documents. The method further includes generating multiple multichannel field images to include the multiple relevancy masks and at least one of the multiple documents or the multiple templates. The method further includes training a second machine learning model based on the multiple multichannel field images and multiple non-native texts associated with the multiple documents. The method further includes executing the second machine learning model to generate multiple non-native texts from the multiple multichannel field images.

An information processing apparatus capable of displaying an image on a predetermined display unit, includes: a reception unit that receives a written input on an image according to an operation of a user in a state where the image is displayed on the display unit; a generation unit that generates a written object according to the written input received by the reception unit; a reference detection unit that detects a reference direction of the image displayed on the display unit; a correction unit that corrects the written object on the basis of the reference direction detected by the reference detection unit; and a display control unit that displays the written object generated by the generation unit.

Biometric data are obtained from biometric sensors on a stand-alone computing device, which may contain an ASIC, connected to or incorporated within it. The computing device and ASIC, in combination or individually, capture biometric samples, extract biometric features and match them to one or more locally stored, encrypted templates. The biometric matching may be enhanced by the use of an entered PIN. The biometric templates and other sensitive data at rest are encrypted using hardware elements of the computing device and ASIC, and/or a PIN hash. A stored obfuscated Password is de-obfuscated and may be released to the authentication mechanism in response to successfully decrypted templates and matching biometric samples. A different de-obfuscated password may be released to authenticate the user to a remote or local computer and to encrypt data in transit. This eliminates the need for the user to remember and enter complex passwords on the device.