Optical character recognition (OCR) method has been used in converting printed text into editable text. OCR is very useful and popular method in various applications. Accuracy of OCR can be dependent on text preprocessing and segmentation algorithms. Sometimes it is difficult to retrieve text from the image because of different size, style, orientation, complex background of image etc. We begin this paper with an introduction of Optical Character Recognition (OCR) method, History of Open Source OCR tool Tesseract, architecture of it and experiment result of OCR performed by Tesseract on different kinds images are discussed. We conclude this paper by comparative study of this tool with other commercial OCR tool Transym OCR by considering vehicle number plate as input. From vehicle number plate we tried to extract vehicle number by using Tesseract and Transym and compared these tools based on various parameters.

Traffic control and vehicle owner identification has become major problem in every country. Sometimes it becomes difficult to identify vehicle owner who violates traffic rules and drives too fast. Therefore, it is not possible to catch and punish those kinds of people because the traffic personal might not be able to retrieve vehicle number from the moving vehicle because of the speed of the vehicle. Therefore, there is a need to develop Automatic Number Plate Recognition (ANPR) system as a one of the solutions to this problem. There are numerous ANPR systems available today. These systems are based on different methodologies but still it is really challenging task as some of the factors like high speed of vehicle, non-uniform vehicle number plate, language of vehicle number and different lighting conditions can affect a lot in the overall recognition rate. Most of the systems work under these limitations. In this paper, different approaches of ANPR are discussed by considering image size, success rate and processing time as parameters. Towards the end of this paper, an extension to ANPR is suggested.

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Recommendation system provides the facility to understand a person's taste and find new, desirable content for them automatically based on the pattern between their likes and rating of different items. In this paper, we have proposed a recommendation system for the large amount of data available on the web in the form of ratings, reviews, opinions, complaints, remarks, feedback, and comments about any item (product, event, individual and services) using Hadoop Framework. We have implemented Mahout Interfaces for analyzing the data provided by review and rating site for movies.

The 2,6-, 2,7-, and 2,8-bis(trifluoromethyl)-a-(2-piperidyl)qumolmemethanols, and the 6-methoxy derivative of the latter, have been synthesized from the appropriate 4-quinolones, through the 4-bromoquinolines, CO-2 carboxylations of the 4-Li derivatives, additions of 2-PyrLi, and H2,/Pt reductions of the resulting pyridyl ketones. An attempt to obtain the 2,5-bis(trifluoromethyl) analog utilized the corresponding 4-quinolone formed as a byproduct in the s3rnthesis of the 2,7 isomer; addition of the 4-Li derivative to 2-p3rridaldehyde gave the a-pyridylmethanol, but subsequent H2/Pt reduction of this gave only the 4-dihydroquinolone-a-(2-piperidyl)methanol.