Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:   present a novel approach for fast search of handwritten Arabic word-parts within large lexicons. The algorithm runs through three steps to achieve the required results. First it warps multiple appearances of each word-part in the lexicon for embedding into the same euclidean space. The embedding is done based on the warping path produced by the Dynamic Time Warping (DTW) process while calculating the similarity distance. In the next step, all samples of different word-parts are resampled uniformly to the same size. The $kd$-tree structure is used to store all shapes representing word-parts in the lexicon. Fast approximation of $k$-nearest neighbors generates a short list of candidates to be presented to the next step. In the third step, the Active-DTW~\cite{sridha99} algorithm is used to examine each sample in the short list and give final accurate results.  demonstrate our method on a database of $23,500$ images of word-parts extracted from the IFN/ENIT and 22,000 images collected from 93 writers. Our method achieves a speedup of 5 orders of magnitude over the exact method, at the cost of only a 3.8\% reduction in accuracy.
['Raid Saabni', 'Alexander M. Bronstein']
I am writing a research paper on a idea Fast Key-Word Searching via Embedding and Active-DTW
[{"from": "human", "value": "I am writing a research paper on a idea Fast Key-Word Searching via Embedding and Active-DTW"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:   present a novel approach for fast search of handwritten Arabic word-parts within large lexicons. The algorithm runs through three steps to achieve the required results. First it warps multiple appearances of each word-part in the lexicon for embedding into the same euclidean space. The embedding is done based on the warping path produced by the Dynamic Time Warping (DTW) process while calculating the similarity distance. In the next step, all samples of different word-parts are resampled uniformly to the same size. The $kd$-tree structure is used to store all shapes representing word-parts in the lexicon. Fast approximation of $k$-nearest neighbors generates a short list of candidates to be presented to the next step. In the third step, the Active-DTW~\\cite{sridha99} algorithm is used to examine each sample in the short list and give final accurate results.  demonstrate our method on a database of $23,500$ images of word-parts extracted from the IFN/ENIT and 22,000 images collected from 93 writers. Our method achieves a speedup of 5 orders of magnitude over the exact method, at the cost of only a 3.8\\% reduction in accuracy."}]