or an English article, the frequency of occurrence of 26 lowercase letters is counted, and then they are encoded by Hoffman code. [Basic Requirements] 1) Read the original text file from the file and display the text on the screen.. 2) Output the number of occurrences of 26 English lowercase letters, and the corresponding Hoffman code...
Huffman Code
[Problem Description]
For an English article, the frequency of occurrence of 26 lowercase letters is counted, and then they are encoded by Hoffman code.
[Basic Requirements]
1) Read the original text file from the file and display the text on the screen..
2) Output the number of occurrences of 26 English lowercase letters, and the corresponding Hoffman code... HERE IS MY original TEXT to be displayed.. At present, most of the dynamic sign language recognition is only for sign language words,
the continuous sign language sentence recognition research and the corresponding results
are less, because the segmentation of such sentence is very difficult. In this paper, a sign language
sentence recognition
as the basic unit of sign word, therefore, according to the key frames we can get related vocabularies,
and thus we can further organize these vocabularies into meaningful sentence. Such work can avoid
the hard point of dividing sign language sentence directly. With the help of Kinect, i.e. motion-control
device, a kind of self- adaptive algorithm of key frame extraction based on the trajectory of sign language
is brought out in the paper. After that, the key frame is given weight according to its semantic contribution.
Finally, the recognition algorithm is designed based on these weighted key frames and thus get the continuous
sign language sentence. Experiments show that the algorithm designed in this paper can realize real-time
recognition of continuous sign language sentences.
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