1. A method for learning one or more parameters of a Convolutional Neural Networks (CNN) used to recognize one or more characters included in a text in a scene text image of training set, comprising steps of: a training apparatus, if segmented character images are acquired by dividing an image of the text into separate images of the characters, generating or allowing another device to generate each multidimensional feature vector corresponding to each of the segmented character images; the training apparatus, on the condition that a specific character image and its neighboring character images are included in the segmented character images, processing or allowing another device to process multidimensional feature vectors ci+j of at least part of the neighboring character images by executing at least one of computations to thereby generate a support vector to be used for recognizing the specific character image, wherein index j is not equal to 0 and −m≤j≤n, and wherein m and n are positive integers; the training apparatus obtaining or allowing another device to obtain a merged vector or its processed value by executing a computation with the support vector and a multidimensional feature vector ci of the specific character image; the training apparatus (i) determining or allowing another device to determine that the specific character image is a specific letter included in a predetermined set of letters by referring to the merged vector or its processed value; and (ii) adjusting or allowing another device to adjust the parameters by referring to a result of the classification.
1. A method for learning one or more parameters of a Convolutional Neural Networks (CNN) used to recognize one or more characters included in a text in a scene text image of training set, comprising steps of: a training apparatus, if segmented character images are acquired by dividing an image of the text into separate images of the characters, generating or allowing another device to generate each multidimensional feature vector corresponding to each of the segmented character images; the training apparatus, on the condition that a specific character image and its neighboring character images are included in the segmented character images, processing or allowing another device to process multidimensional feature vectors ci+j of at least part of the neighboring character images by executing at least one of computations to thereby generate a support vector to be used for recognizing the specific character image, wherein index j is not equal to 0 and −m≤j≤n, and wherein m and n are positive integers; the training apparatus obtaining or allowing another device to obtain a merged vector or its processed value by executing a computation with the support vector and a multidimensional feature vector ci of the specific character image; the training apparatus (i) determining or allowing another device to determine that the specific character image is a specific letter included in a predetermined set of letters by referring to the merged vector or its processed value; and (ii) adjusting or allowing another device to adjust the parameters by referring to a result of the classification. 3. The method of claim 1 , before the step of generating or allowing another device to generate each multidimensional feature vector corresponding to each of the segmented character images, further comprising a step of: the training apparatus, if the scene text image is inputted, detecting and extracting or allowing another device to detect and extract the image of the text from the scene text image, and segmenting or allowing another device to segment the image of the text.