Abstract: A character recognition method may include at least the following steps. A location step may include acquiring an image and locating a character region of the image. The character region may include a character and a local background. The method may further include a background judgment step for determining whether the local background is a complex background; a determination step for determining a color of the character if the local background is a complex background; a construction step for constructing a mask for the character by combining the color of the character and a character region; and a first recognition step for extracting the character from the character region by using the mask, recognizing the character, and outputting the recognition result. A character recognition device is further provided.

Abstract: A character recognition method may include at least the following steps. A location step may include acquiring an image and locating a character region of the image. The character region may include a character and a local background. The method may further include a background judgment step for determining whether the local background is a complex background; a determination step for determining a color of the character if the local background is a complex background; a construction step for constructing a mask for the character by combining the color of the character and a character region; and a first recognition step for extracting the character from the character region by using the mask, recognizing the character, and outputting the recognition result. A character recognition device is further provided.

Abstract: An object detection and classification method and apparatus, the method includes: inputting an image; performing window scan on the image to make, for each class, object existence decision on each window, and making, on a window of which the decision is positive, an object class decision to obtain an object classification confidence of the window; performing, for all classes, spatial neighborhood merging on all windows with positive result of object existence decision to obtain merged regions and object detection confidences thereof; judging, for each merged region, whether the object detection confidence of the merged region is higher than a predetermined threshold; calculating, for each class, if higher than the predetermined threshold, a merged object classification confidence for all the windows with positive result of object existence decision within the merged region; and determining a class with a highest merged object classification confidence as the class of the merged region.

Abstract: The present invention provides a device and method for multiclass object detection, wherein the detection device includes: an input unit configured to input data to be detected; and a joint classifier within which a plurality of strong classifiers capable of processing multiclass object data are included, wherein each of the strong classifiers is acquired by adding a set of weak classifiers together, and each weak classifiers performs a weak classification for the data to be detected by using a feature. A list of shared features is included within the joint classifier, and each feature within the list is shared by one or more weak classifiers belonging to different strong classifiers respectively; and the weak classifiers, which use a same feature and belong to different strong classifiers respectively, have different parameter values from one another.