FORM 2 THE PATENTS ACT, 1970 & THE PATENT RULES, 2003 COMPLETE SPECIFICATION (See Section 10 and Rule 13) Title of invention: SYSTEM AND METHOD FACILITATING DESIGNING OF CLASSIFIER WHILE RECOGNIZING CHARACTERS IN A VIDEO Applicant: Tata Consultancy Services Limited A company Incorporated in India under The Companies Act, 1956 Having address: Nirmal Building, 9th Floor. Nariman Point. Mumbai 400021, Maharashtra, India The following specification particularly describes and ascertains the nature of this invention and the manner in which it is to be performed:- CROSS-REFERENCE TO RELATED APPLICATIONS AND PRIORITY [001] The present application claims priority from Indian Patent application number 3308/MUM/2012 filed on 16th of November 2012. TECHNICAL FIELD [002] The present disclosure in general relates to designing of a hierarchy of features. More particularly, the present disclosure relates to the designing of the hierarchy of features while recognizing text characters in a video. BACKGROUND [003] Recent market report on Consumer Electronics shows that TV with Internet is going to be one of the most demanding products for the near future. As a consequence the demand of recognizing the TV context also increases among the research community. Lots of information about the TV video may be obtained from the meta data in case of digital TV broadcast. But in the developing countries like India, still the penetration of digital TV is near about 10%. [004] Thus Video OCR remains an unsolved problem still now as there is no existing solution that can address the following problems: [005] Video OCR needs to process images/frames of a video with maximum 720x576 pixels. The resolution here is very poor compared to the document images which are mostly scanned at 300 dots per inch (dpi) or the images captured using 8 mega pixel camera. [006] Video OCR need not reproduce the fonts since the main intention is to recognize the text to get the context. On the other hand, document image OCRs may need to recognize the font too. [007] In an observation, font variation in the text of different TV channels is very little. The observation may be justified by a fact that the texts are generated and blended over the video content using some hardware and the variation of such hardware used in the studios is very less as the number of manufacturer of such hardware is very less. SUMMARY OF THE INVENTION [008] This summary is provided to introduce aspects related to system(s) and method(s) for designing a classifier while recognizing texts from videos and the aspects are further described below in the detailed description. This summary is not intended to identify essential features of the claimed subject matter nor is it intended for use in determining or limiting the scope of the claimed subject matter. [009] The present disclosure relates to a method to facilitate designing of a hierarchy of feature while recognizing one or more characters in a video. The method comprises of collecting one or more features from each of the segments in a video frame, the video frame having been extracted from a video and divided into multiple segments. The method further comprises of preparing multidimensional feature vectors by using the one or more features. The multi-dimensional feature vectors are used to classify the one or more characters present in the video frame. The method further comprises of calculating a minimum distance between the multi-dimensional features vectors of a test character and the multi-dimensional feature vectors of a pre-stored character template. The test character is the character to be classified and is present in the video and the minimum distance is a distance representing closeness between the characters.. The method further comprises of designing a hierarchy of the multi-dimensional feature vectors by selecting the feature vectors with respect to a decreasing order of the minimum distance and classifying the characters based on the hierarchy of the feature vectors. [010] The present disclosure also relates to a system to facilitate designing of a hierarchy of features while recognizing one or more characters in a video. The system comprises of a processor and a memory coupled to the processor. The processor is capable of executing a plurality of modules stored in the memory. The plurality of module comprises a collecting module configured to collect one or more features from each of the segments in a video frame, the video frame having been extracted from a video and divided into multiple segments and a preparation module configured to prepare multi-dimensional feature vectors by using the one or more features. The feature vectors are used to classify the one or more characters present in the video frame. The modules further comprises of a calculation module configured to calculate a minimum distance between the multi-dimensional feature vectors of a test character and the multi-dimensional feature vectors of a pre-stored character template. The test character is the character to be classified and is present in the video and the minimum distance is a distance representing closeness between the characters. The module further comprises of a designing module configured to design a hierarchy of the multi-dimensional feature vectors with respect to a decreasing order of the minimum distance and a classification module configured to classify the one or more characters based on the hierarchy of feature vectors so designed. [Oil] The present disclosure also relates to a computer program product having embodied thereon a computer program facilitating designing of a hierarchy of features while recognizing one or more characters in a video. The computer program product comprises of for collecting one or more features from each of the segments in a video frame, the video frame having been extracted from a video and divided into multiple segments and a program code for preparing multi-dimensional feature vectors by using the features. The multi-dimensional feature vectors are used to classify the one or more characters present in the video frame. The computer program product comprises of a program code for calculating a minimum distance between the multi-dimensional feature vectors test character and the multidimensional feature vectors of a pre-stored character template. The test character is the character to be classified and is present in the video and the minimum distance is a distance representing closeness between the characters. The program code for designing the hierarchy of the feature vectors by selecting the multi-dimensional feature vectors with respect to a decreasing order of the minimum distance and a program code for classifying the characters based on the hierarchy of the feature vectors. BRIEF DESCRIPTION OF DRAWINGS [012] The detailed description is described with reference to the accompanying figures. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The same numbers are used throughout the drawings to refer like features and components. [013] Figure 1 illustrates a network implementation of a system to provide designing of classifier while recognizing one or more characters in a video is shown, in accordance with an embodiment of the present subject matter. [014] Figure 2 illustrates the systemto provide designing of classifier while recognizing one or more characters in a video, in accordance with an embodiment of the present subject matter. [015] Figure 3 illustrates a method to provide designing of classifier while recognizing one or more characters in a video, in accordance with an embodiment of the present subject matter. DETAILED DESCRIPTION [016] While aspects of described system and method facilitating designing of a hierarchy of features while recognizing texts from videos may be implemented in any number of different computing systems, environments, and/or configurations, the embodiments are described in the context of the following exemplary system. [017] Referring now to Figure I, a network implementation 100 of system 102 facilitating designing of a hierarchy of features while recognizing text in a video is shown, A video frame from the video is extracted and is divided into segments. Features are collected from each segment in the video frame and feature vectors are prepared. The feature vectors are selected based on a minimum distance. The feature vectors are used to generate a hierarchy of feature vectors to classify the text characters present in the video frame. [018] Although the present subject matter is explained considering that the system 102 is implemented as an application on a server, it may be understood that the system 102 may also be implemented in a variety of computing systems, such as a laptop computer, a desktop computer, a notebook, a workstation, a mainframe computer, a server, a network server, and the like. It will be understood that the system 102 may be accessed by multiple users through one or more user devices 104-1, 104-2...104-N, collectively referred to as user 104 hereinafter, or applications residing on the user devices 104. Examples of the user devices 104 may include, but are not limited to, a portable computer, a personal digital assistant, a handheld device, and a workstation. The user devices 104 are communicatively coupled to the system 102 through a network 106. [019] In one implementation, the network 106 may be a wireless network, a wired network or a combination thereof. The network 106 can be implemented as one of the different types of networks, such as intranet, local area network (LAN), wide area network (WAN), the internet, and the like. The network 106 may either be a dedicated network or a shared network. The shared network represents an association of the different types of networks that use a variety of protocols, for example, Hypertext Transfer Protocol (HTTP), Transmission Control Protocol/Internet Protocol (TCP/IP), Wireless Application Protocol (WAP), and the like, to communicate with one another. Further the network 106 may include a variety of network devices, including routers, bridges, servers, computing devices, storage devices, and the like. [020] Referring now to Figure 2, the system 102 is illustrated in accordance with an embodiment of the present subject matter. In one embodiment, the system 102 may include at least one processor 202, an input/output (I/O) interface 204, a memory 206. The at least one processor 202 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the at least one processor 202 is configured to fetch and execute computer-readable instructions stored in the memory 208. [021] The I/O interface 204 may include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, and the like. The I/O interface 204 may allow the system 102 to interact with a user directly or through the client devices 104. Further, the I/O interface 204 may enable the system 102 to communicate with other computing devices, such as web servers and external data servers (not shown). The I/O interface 204 can facilitate multiple communications within a wide variety of networks and protocol types, including wired networks, for example. LAN, cable, etc., and wireless networks, such as WLAN, cellular, or satellite. The I/O interface 204 may include one or more ports for connecting a number of devices to one another or to another server. [022] The memory 206 may include any computer-readable medium known in the art including, for example, volatile memory, such as static random access memory (SRAM) and dynamic random access memory (DRAM), and/or non-volatile memory, such as read only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes. The memory 206 may include modules 208 and data 222. [023] The modules 208 include routines, programs, objects, components, data structures, etc., which perform particular tasks, functions or implement particular abstract data types. In one implementation, the modules 208 may include a collection module 210, a preparation module 212, a calculation module 214, a designing module 216 and a classification module 218. The other modules 220 may include programs or coded instructions that supplement applications and functions of the system 102. [024] The data 222, amongst other things, serves as a repository for storing data processed, received, and generated by one or more of the modules 208. The data 222 may also include a database 224, and other data 226. The other data 226 may include data generated as a result of the execution of one or more modules in the other module 220. [025] The present disclosure relates to a system 102 to provide a low complexity embeddable OCR (Optical Character Recognition) to recognize features. The features comprises of numerals and some small set of special characters like ", "(" etc- Such type of features are often seen in videos like sports video, information in various shows like recipe shows, news video clips, subtitle video text in video frame etc. [026] The collection module 210 collects one or more features from the multiple segments in the video frame. The Video frame having been extracted from a video and divided into multiple segments. [027] The preparation module 212 prepares multi-dimensional feature vectors by using the features. The multi-dimensional feature vectors (or simply feature vectors for simplicity) are used to classify the characters present in the video. The feature vectors comprises of Vertical Projection (Fl), Horizontal Projection (F2), Contour (F3). and Stroke Direction (F4). [028] The calculation module 214 computes a minimum distance as Euclidian distance as a classifier to reduce the complexity. The minimum distance is a distance representing closeness between the characters. The Euclidian distance does not put any significant effect on the recognition accuracy. The system 102 performs two major steps in designing a hierarchy of features while recognizing characters in the video. The hierarchy of the feature vectors is designed with respect to a decreasing order of the minimum distance. The two major steps are template creation phase (giving pre-stored template here) and a testing phase. In the template creation phase, a 448 dimensional feature vector is extracted for each character and the 448 dimensional feature vectors are stored as template. In the testing phase, the features are extracted after designing a hierarchy of the features. After each level present in the hierarchy of features to be followed for recognizing characters (text characters) in the video. Euclidian distance between each feature at each level in the hierarchy and the character in the template is calculated. The text characters are mapped to a point in a higher dimension. The pre-stored template is used for mapping text characters. The characters in the template may be represented as a point in the same vector space. [029] The calculation module 214 calculates the Euclidian distance (dl.τ i) between a test character and each of the i'h character from the template characters. The test character is the is the ith character if (dl.τ i) < (dl.τj) Vj€ template-set and j= i and dl.τ i