Disclosed are methods for handwriting recognition. In some aspects, an image representing a page of a sample document is analyzed to identify a region having indications of handwriting. The region is analyzed to determine frequencies of a plurality of geometric features within the region. The frequencies may be compared to profiles or histograms of known language types, to determine if there are similarities between the frequencies in the sample document relative to those of the known language types. In some aspects, machine learning may be used to characterize the document as a particular language type based on the frequencies of the geometric features.

An information processing apparatus is configured to cause a first recognizer to execute a first recognition process that takes sensor information as input, and a second recognizer to execute a second recognition process that takes the sensor information as input, the second recognizer being configured to operate under different capability conditions from the first recognizer; determine one of a transmission necessity and a transmission priority of the sensor information depending on a difference between a first recognition result of the first recognition process and a second recognition result of the second recognition process; and transmit the sensor information to a server apparatus according to the determined one of the transmission necessity and the transmission priority.

Embodiments may provide techniques that provide identification of images that can provide reduced resource utilization due to reduced sampling of video frames for visual recognition. For example, in an embodiment, a method of visual recognition processing may be implemented in a computer system comprising a processor, memory accessible by the processor, and computer program instructions stored in the memory and executable by the processor, the method comprising: coarsely segmenting video frames of video stream into a plurality of clusters based on scenes of the video stream, sampling a plurality of video frames from each cluster; determining a quality of each cluster, re-clustering the video frames of video stream to improve the quality of at least some of the clusters.

A text correction method and apparatus can take advantage of a greatly reduced number of error-ground truth pairs to train a deep learning model. To generate these error-ground truth pairs, different characters in a ground truth word are replaced with a symbol, not appearing in any ground truth words, to generate error words which are paired with that ground truth word to provide error-ground truth word pairs. This process may be repeated for all ground truth words for which training is to be performed. In embodiments, pairs of characters in a ground truth word may be replaced with a symbol to generate the error words which are paired with that ground truth word to provide error-ground truth word pairs. Again, this process may be repeated for all ground truth words for which training is to be performed.

A system performs processing of biometric information to create multiple templates. This allows biometric systems to be flexible and interact with a plurality of vendors' technologies. Specifically, a biometric sample is captured from a sensor and transmitted to a processing component. The biometric sample is then processed by a first algorithm to yield a biometric template and the template is stored and associated with a record identifier. The biometric sample is also processed by a second algorithm to yield a second template. The second template is stored and associated with the record identifier.

Methods and systems are provided for distinguishing between more than one fluorescent species present in a sample in fluorescence microscopy. The method involves illuminating the sample with at least one light source. More than two images of the illuminated sample are recorded over a period of time, each image comprising a plurality of pixels, wherein each pixel corresponds to a location in the sample and records a degree of fluorescence at the location in the sample at a particular point in time. A photostability characteristic of the degree of fluorescence at each pixel over the period of time over which the more than two images were recorded is determined and used to distinguish between the more than one fluorescent species present in the sample.

A method for estimating text heights of text line images includes estimating a text height with a sequence recognizer. The method further includes normalizing a vertical dimension and/or position of text within a text line image based on the text height. The method may also further include calculating a feature of the text line image. In some examples, the sequence recognizer estimates the text height with a machine learning model.

The present application discloses a method and an apparatus for recognizing text content, and an electronic device, and relates to a text recognition technique in the field of computer technology. The specific implementation is as follows: acquiring a dial picture; detecting at least one text centerline and a bounding box corresponding to each text centerline in the dial picture; and recognizing text content in each line of text in the dial picture based on the at least one text centerline and the bounding box corresponding to each text centerline.

According to certain embodiments, an electronic apparatus comprises: a memory storing a generator previously trained to generate a prediction image based on one or more input images; and a processor configured to: acquire feature data from a plurality of image frames input through at least one layer included in the generator, extract feature data corresponding to change over time from the feature data acquired through an attention layer included in the generator, and acquire a prediction image frame by inputting the extracted feature data to at least one other layer included in the generator.

A method, apparatus, non-transitory computer readable medium, and system for panoptic segmentation are described. Embodiments may generate a feature pyramid for an input image, wherein the feature pyramid comprises a plurality of feature maps at different resolution levels, apply an attention module to the feature pyramid to produce an enhanced feature map, combine the enhanced feature map with each of the plurality of feature maps to produce an enhanced feature pyramid, generate semantic information for the input image based on the enhanced feature pyramid, generate a plurality of object regions based on the enhanced feature pyramid, generate instance information for each of the plurality of object regions, and generate panoptic segmentation information for the input image based on the semantic information and the instance information for each of the plurality of object regions.