Please select the most similar patent number from A, B, C and D. Which number is?
A character string segmentation method, apparatus and device, said method comprising: acquiring forward segmentation results of a character string to be segmented, said forward segmentation results comprising at least one first word (101); acquiring backward segmentation results of the character string to be segmented, said backward segmentation results comprising at least one second word (102); acquiring a word frequency of said first word and a word frequency of said second word, said word frequencies being the number of times predetermined words appear in a predetermined text (103); determining segmentation results of said character string to be segmented according to the word frequency of said first word and the word frequency of said second word, wherein the segmentation results of said character string to be segmented are said forward segmentation results or said backward segmentation results (104). Not only does the present invention improve the success rate of segmentation, but also increases the probability of the semantics of words being accurate within the segmentation results.
A: A text information processing method, device and terminal, wherein the method comprises: determining a pinyin character string corresponding to text information to be processed (101); using an N-tuple algorithm to convert the pinyin character string into a character string set that comprises a plurality of character string elements (102); determining an index position and the number of occurrences, in a total set of character strings, of each character string element in the character string set (103); generating a pinyin hash vector corresponding to the text information to be processed according to the index position and number of occurrences corresponding to each character string element (104); and processing the pinyin hash vector by means of an embedded neural network to obtain continuous features corresponding to the text information to be processed (105). Since the pinyin hash space is adopted to characterize words in a lexicon, there is good robustness for words that do not appear in the lexicon.
B: The present disclosure relates to a water-body segmentation method and apparatus, an electronic device, and a storage medium. The method comprises: segmenting, according to spectrum information of a first target image, the first target image to obtain a water-body region, and acquiring a first water-body segmentation result of the first target image; segmenting, by means of at least one preset water-body segmentation technique, the first target image to obtain a water-body region, and acquiring at least one second water-body segmentation result; and determining, according to the first water-body segmentation result and the at least one second water-body segmentation result, a target water-body segmentation result of the first target image.
C: Disclosed are a license plate verification method and apparatus, and a computer device and a storage medium. Different deviation comparison modes are determined by means of the result of comparing the length of a target segmentation character string with the length of a segmentation character string to be verified; a first target comparison position and a second target comparison position are updated according to the deviation comparison mode by comparing the character of the target segmentation character string with the character of the segmentation character string to be verified; when the first target comparison position is the last position, the number of character verification errors is adjusted according to the second comparison result of the last position. By comparing the preset number of allowable character verification errors with the number of character verification errors, the influence of errors occurring in the license plate recognition process on license plate verification is well avoided, so that the pass rate and accuracy of license plate recognition are improved, and the misjudgment rate of license plate verification is reduced.
D: An image region recognition method, comprising: obtaining an image to be segmented, wherein said image comprises a plurality of extreme points (101); generating first image feature information according to said image, wherein the first image feature information comprises N image matrices and a first heat map, the first heat map being generated according to the plurality of extreme points (102); obtaining, by means of a first image segmentation model, a first image segmentation region corresponding to the first image feature information (103); obtaining a second heat map according to annotation points corresponding to the first image segmentation region, and the first image segmentation region (104); obtaining, by means of a second image segmentation model, a second image segmentation region corresponding to said image (105); and generating an image recognition result of said image according to the second image segmentation region (106). According to the method, by means of auxiliary segmentation in the second stage, the region having a poor effect in the image segmentation in the first stage is further segmented, so as to obtain a more accurate image segmentation result, thereby improving the performance of image segmentation.
Answer: A