Objective

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Uyghur belongs to adhesive language

and the formation and meaning of words in Uyghur language depend on affix connection

which add affixes to the stems to achieve different semantic meaning. For example

in Uyghur "مەكتەپنى" refers to school

and it could be added by a first person singular in Uyghur "ىر" as a suffix to form a new word "بەرسىڭىز"

which means my school. During adding suffixes

certain morphological changes will occur at the tail of the stems. In addition

phonetic change also happens at that process

such as weakling (some morphological changes occur at the tail of a stem)

epenthesis (a few characters were added at the tail of the stem)

and deletion(a few characters were deleted at the tail of the stem). Multiple phonetic changes also appear simultaneously

causing further morphological change. All the semorphological changes form the word different from the stem. Therefore

using the current word spotting technique

only a specific Uyghur vocabulary can be retrieved

and a certain stem cannot search its corresponding suffixed words. In addition

traditional word spotting approaches only aim at the number of the matching sets and noton the spatial relationship of the matching sets. Therefore

some drawbacks for word spotting technique occur. This study proposes Uyghur-printed suffix word retrieval based on mapping relationship that take advantage of the spatial relationship of the matching sets to retrieve the corresponding suffix words of the stems.

Method

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The process of the proposed approach is described as follows.First

the segmentation algorithm segments printed the Uyghur document images to word image corps. Then

the local features of the Uyghur word image are extracted. To compare the efficiency of the different local features

scale-invariant feature transform(SIFT) and speeded up robust features(SURF) have been adopted. The experiment result shows that SIFT feature has better performance than SURF because SIFT can obtain more feature points than SURF

and the distribution of SIFT feature points is more diverse than SURF

which is very helpful for further retrieval steps. However

SURF is more efficient than SIFT considering the time efficiency. Then

Brute-Force matching and fast library for approximate nearest neighbor(FLANN) bilateral feature matching have been adopted as matching algorithm. The experiment result shows that FLANN bilateral feature matching has better performance than Brute-Force matching because FLANN bilateral feature matching can filter more mismatch pairs than Brute-Force matching. In addition

the correctness of the feature matching set is very important to the following suffix word retrieval

and the accuracy of FLANN bilateral feature matching is very outstanding. Finally

the feature matching sets are subjected to homography transformation and perspective transformation to the Uyghur word image for the final retrieval steps. After homography transformation and perspective transformation

a quadrilateral is built. If this quadrilateral belongs to rectangle and the right part of the acquired rectangle simply match with the outline of the query word image

then the retrieved word belongs to corresponding suffixed words of the query stem.Meanwhile

if this quadrilateral does not belong to rectangle and does not match with the outline of the query word image

then the retrieved word does not belong to the corresponding suffixed words of the query stem but to the mismatch. This result indicates that the proposed method not only can retrieve corresponding suffixed words of the query word but also can filter the mismatch word. In other words

the feature matching sets are transformed into a spatial relationship and are further determined whether the retrieved word belongs to suffix word or mismatched word according to the spatial relationship. The spatial relationship of the feature matching set is searched for the suffix word

thereby implementing the printed Uyghur Text image suffix word retrieval.

Result

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The experimental data selected 17 648 segmented word images in 190 Uyghur print text images and 30 word images

which have 167 corresponding suffix words considered as the search terms. In the experiment

we used different local feature algorithms to suffix retrieval. The comparison results show that the SIFT algorithm's suffix retrieval effect is better than SURF algorithm

and its accuracy and recall rate reach 94.23% and 88.02%

respectively. In addition

we carried out comparative experiments in different situations

such as weakling

epenthesis

deletion

and replacement. Moreover

multiple phonetic changes appear simultaneously and change in the tail of the word stem. In those five different situations

the retrieval result of changes in the tail of the word stem was the best

and its accuracy and recall rate reach 98.9% and 96.07%

respectively. The main reason for this result is that the changes in the tail of the word stem do not make very obvious formation changes of the stem word

which can be very helpful for the suffix word retrieval. However

in different cases

multiple phonetic changes appear simultaneously that the accuracy and recall rate of the retrieval reach 66.6% and 22.2%. The reason for such low performance is that multiple phonetic changes simultaneously change the formation of the stem part in the suffix word.In addition

several characters in the stem part have been replaced by other characters

which are very difficult to retrieve its corresponding suffix word by the original stem word. However

this kind of situation only takes few percentages of the whole morphological changes.

Conclusion

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Therefore

the proposed algorithm meets the different retrieval needs of users. The method of suffix word retrieval based on mapping relationship is also the first implementation of Uyghur suffix word retrieval method. The Uyghur suffix image is efficiently retrieved by the spatial relationship between the matching sets.