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like commodity, crude just to name a few [12]. Unlike other Abstract-Stock market analysis and prediction has been one technical indicators, which try to find statistical relation of of the widely studied and most interesting time series analysis current price with future price, candlestick indicator tries to problems till date. Many researchers have employed many find the investor's sentiment on a given stock. The idea is different models, some of them are linear statistic based while simple "what will be the price of a stock tomorrow depends some non linear regression, rule, ANN, GA and Fuzzy logic on what people think about that stock at the end of the day". based. In this paper we have proposed a novel model that tries to I o .. v predict short term price fluctuation, using Candlestick Analysis. Iode tO indenif as a pattern s, we have This is a proven technique used for short term prediction of stock used SOM and CBR as a pattern recognizer and pattern price fluctuation and market timing since many years. Our matching mechanism. Decision derived by the CBR (SOM) is approach has been hybrid that combines Self Organizing Map in turn combined with other technical indicator like with Case Based Reasoning to indentify profitable patterns Stochastics and Volume, in order to improve the accuracy of (candlestick) and predicting stock price fluctuation based on the predication. pattern consequences. All available models for predicting stock price behavior can be divided into three category 1) Machine Learning based Index Terms-Candlestick Analysis, Short Term Stock (Black box), 2) Expert knowledge based (White box) and 3) Prediction, Self Organizing Map, Case-Base Reasoning Combination of both (Hybrid). In Black box models [3] [5] the system learns itself from previous experiences. Such a model does not require an expert, but the representation of the knowledge is complex and not verifiable (like ANN) where as S tock data analysis has remain one of the challenging time in White box models [2] [4] [6] the representation of the series analysis problem over the years. Because of the knowledge is simple and verifiable but requires expert to complexity and intrications, the problem has received impart knowledge in the system (like Fuzzy rule, CBR). attention from many researchers. Owing to its high expected System is only as good as the expert's knowledge. Hybrid returns, there are many starting from local investor to a big models [7] [8] try to combine the benefits of both. In this paper finance houses who are interested in finding out how the stock we have proposed a hybrid model which combines SOM with market works? Many techniques have been derived over a CBR. period of time by many researchers and investors to predict

Abstract. This paper presents a Structural feature based method for classification of printed Gujarati characters. The ability to provide incremental definition of characters in terms of its native components makes the proposal unique and versatile. It deals with varied sizes, font styles, and stoke widths. The features are validated on subset of machine printed Gujarati characters using a simple rule based classifier and the initial results are encouraging.

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This article presents an elegant technique for extracting the low-level stroke features, such as endpoints, junction points, line elements, and curve elements, from offline printed text using a template matching approach. The proposed features are used to classify a subset of characters from Gujarati script. The database consists of approximately 16,782 samples of 42 middle-zone symbols from the Gujarati character set collected from three different sources: machine printed books, newspapers, and laser printed documents. The purpose of this division is to add variety in terms of size, font type, style, ink variation, and boundary deformation. The experiments are performed on the database using a k-nearest neighbor (kNN) classifier and results are compared with other widely used structural features, namely Chain Codes (CC), Directional Element Features (DEF), and Histogram of Oriented Gradients (HoG). The results show that the features are quite robust against the variations and give comparable performance with other existing works.