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Ahead Lehman Khan & Dzulkilfli Muhammad A Simple Segmentation Approach for Unconstrained Cursive Handwritten Words in Conjunction with the Neural Network. Ahead Lehman Khan PhD researcher Department

This paper deals a small dataset to analyze the results of an earlier study on concatenating words. This dataset consists of 26,000 words in which a concatenated word can be generated. This is the best example of a dataset on which one can analyze the generalizations that a neural network can generate. To concatenate is generated through the word-matching. This analysis was performed for a small dataset containing words, concatenated and for which there was a word-match on which the concatenated word was generated. The best example of an example of an unsupervised neural network is given in Table 2. The result from this analysis is shown in Figure 3. The data is presented in two sections (S1 & S2). The first section deals with the generalization from the generalization class to words in the second section. The generalization in the second section is done from a set of 1,000 of the word-reversed. The class of the second section is therefore: “Constrained.” The second section (S1) consists of the first 20,000 words in the data set. The first 20k are the words which can be re-transformed. The second 20k include the words in S2. The results presented in S1 (shown in Figure 3) and the class for the second section (S2) are given in Table 1. Table 1: Unsupervised Neural Network Classification for a Small Training Data Set The Classification of concatenating words in this dataset is also presented in Figure 4. Figure 4: Unsupervised Unconstrained Neural Network Classification Acknowledgment Thanks to Mr Jersey van der CLIS for giving permission to publish this article. Lecture Notes The full paper is now available online at: The paper deals a small dataset to analyze the results of an earlier study on concatenating words. This dataset consists of 26,000 words in which a concatenated word can be generated. This is the best example of a dataset on which one can analyze the generalizations that a neural network can generate. To concatenate is generated through the word-matching. This analysis was performed for a small dataset containing words, concatenated and for which there was a word-match on which the concatenated word was generated. The best example of an example of an unsupervised neural network is given in Table 2.

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