Indian Currency Recognition and Verification using Image Processing

Abstract

In India the day to day transactions are increasing over the past few years , which gives the way of increasing currency transactions in day to day life. These increasing transactions cause to increase in currency traverse. In advantage of that the fake currency of Rs 50,100,500,1000 is been produced , but after the demonetization the counterfiet notes of new Rs 50,200,500,2000 is increased a lot. . which in time effects the economic growth of the country. Here in this paper the recognition and verification of the paper currency is explained with the use of image processing techniques. The following approach consist of multiple element transactions like Image Acquisition,Feature extraction&comparison ,Texture features,Voice output. The desired results will be the text and voice output of the currency recognized and verified.

Ke y Words: Currency Recognition, Image Processing Technique, Image segmentation Character extraction, Currency Verification.

1.I NTRODUCTION

Technology is growing very fast these days. Consequently the banking sector is also getting modern day by day. This brings a deep need of automatic currency recognisation . Many researchers have been encouraged to develop robust and efficient automatic currency detection machine [1]. Automatic machine which can detect banknotes are now widely used in dispensers of modern products like candies, soft drinks bottle to bus or railway tickets. The technology of currency recognition basically aims for identifying and extracting visible and invisible features of currency notes. Until now, many techniques have been proposed to identify the currency note. But the best way is to use the visible features of the note [2]. For example, color and size. But this way is not helpful if the note is dirty or torn. If a note is dirty, its color characteristic are changed widely. So it is important that how we extract the features of the image of the currency note and apply proper algorithm to improve accuracy to recognize the note.

The worst hit of this action is mostly average citizen as fake banknotes have become so deeply embedded in the Indian economy that even bank branches and ATMs are disbursing counterfeit currency. From petrol stations to the local vegetable vendor, everybody is wary of accepting banknotes in denominations of Rs.50,200,500 and Rs.2, 000 (which were released after the demonetization) a majority of them are almost impossible to tell from genuine banknotes. The usual effect of counterfeit on economy is inflation.

The only solution that is presently available for common man to detect recognize counterfeit currency is “Fake Note Detector Machine”. This machine is mostly available only in banks which is not reachable every time by average citizen. All these scenarios need a kind of solution for common people to judge a forged bank note and to refrain our currency from losing its value.

We apply here a simple algorithm which works properly. The image of the currency note is captured through a digital camera.processing on the image is done on that acquired image using concepts like image segmentation, edge information of image and characteristics feature extraction[3-4]. MATLAB is the perfect tool for computational work, and analysis.The image formats supported by MATLAB are JPEG, PCX, TIFF, PNG etc.[13][14]. Feature extraction of images is challenging task in digital image processing. It involves extraction of invisible and visible features of Indian currency notes. This approach consists of different steps like image acquisition, edge detection, gray scale conversion, feature extraction, image segmentation and decision making [5-6]. Acquisition of image is process of creating digital images, from a physical scene. Here, the image is captured by a simple digital camera such that all the features are highlighted. Image is then stored for further processing.

1.1.Process of Edge detection

It is a basic tool in image processing. It is widely used in area of feature detection and extraction. This process aim at identifying point in digital image at which image brightness sharply changes.

1.2.Process of Texture Feature Extraction:

It is a tool in image processing. It is used in Haar feature extraction of the digital image depending on the pixel value which is applied to the KNN classifier.

2.RELATED WORK

Features of Indian Currency

There are too many features present in Indian currency which is decided by Reserve Bank of India. Figure 2.1 gives the idea about currency features of 1000 Rs note.

Abbildung in dieser Leseprobe nicht enthalten

3.PROPOSED SYSTEM

The proposed system of this article consists of 2 parts

1. Currency Recognisation
2. Currency Verification

1.Currency Recognisation:

In currency recognition we detect and isolate the denomination of the currency with the help of image processing .Here we are extracting the features of the acquired image . We are pursuing the followed steps.

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Fig.(a)-Flow Chart of Currency Recognisation

1. 1Image A cquisition:

Performing image acquisition in image processing is always the first step in the work flow sequence because, without an image, no processing is possible. After the image has been obtained, various methods of processing can be applied to the image to perform the many different vision tasks. There are various ways to acquire image such as with the help of camera or scanner. Acquired image should retain all the features [7].

1. 2 Pre-processing:

The main goal of the pre-processing to enhance the visual appearance of images and improve the manipulation of data sets. Image pre-processing, also called image restoration, involves the correction of distortion, degradation, and noise introduced during the imaging process. Interpolation is the technique mostly used for tasks such as zooming, rotating, shrinking, and for geometric corrections. Removing the noise is an important step when processing is being performed. However noise affects segmentation and pattern matching [7].

1.3 Edge Detection:

Edge detection is the name for a set of mathematical methods which aim at identifying points in a digital image at which the image brightness changes sharply or, more formally, has these continuities. The points at which image brightness changes sharply are typically organized into a set of curved line segments termed edges. Edge detection is an image processing technique for finding the boundaries of objects within images. It works by detecting discontinuities in brightness. Edge detection is used for image segmentation and data extraction in areas such as image processing, computer vision, and machine vision [8].

1.4 Image Segmentation

Image segmentation is the process partitioning a digital image into multiple Segments (sets of pixels, also known as super pixels). The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze

1.5 Feature Extraction and Comparison :

Feature extraction is a special form of dimensional reduction. When the input data to an algorithm is too large to be processed and it is suspected to be very redundant then the input data will be transformed into a reduced representation set of features. Transforming the input data into the set of features is called feature extraction. If the features extracted are carefully chosen it is expected that the features set will extract the relevant information from the input data in order to perform the desired task using this reduced representation instead of the full size input [7].

1.7 Output

The output of the currency recognisation will be given in the format of text as well as the voice.The tezt output will be given in a text box of GUI.

2.Currency Verification

In currency verification we check or authenticate whether the currency is valid or not. Here we extract the texture features (HAAR) of the currency and compare it with the stored HAAR features, then produce the output.. For the above process we follow below steps.

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Fig.(b)-Flow Chart Of Currency Verification

2.1 Image A cquisition:

Performing image acquisition in image processing is always the first step in the work flow sequence because, without an image, no processing is possible. After the image has been obtained, various methods of processing can be applied to the image to perform the many different vision tasks. There are various ways to acquire image such as with the help of camera or scanner. Acquired image should retain all the features [7].

2.2 Pre-processin g :

The main goal of the pre-processing to enhance the visual appearance of images and improve the manipulation of data sets. Image pre-processing, also called image restoration, involves the correction of distortion, degradation, and noise introduced during the imaging process. Interpolation is the technique mostly used for tasks such as zooming, rotating, shrinking, and for geometric corrections. Removing the noise is an important step when processing is being performed. However noise affects segmentation and pattern matching [7].

2.3 Haar Features Extraction

Haar-like features are digital imaging features which are working only on image intensities (i.e) RGB values of each and every pixel in an image. A Haar-like feature considers neighboring rectangular regions at a definite location in a detection window, each region pixel intensities are summed up also calculates the difference between these sums. Then it is used to categorize subsections of an image. In this work image database with currencies are used. For all currencies the region of the denomination&design is darker than the region of the see through of Gandhi’s portrait is a common observation. So a common haar feature for currency detection is a set of adjacent triangles that lie in plain regions. The region of these triangles is defined relative to a detection window that acts like a bounding box to the target object of the currency note[9].

2.4 Feature Comparison

The main goal is to compare the extracted features with the stored features which are stored in data sets which gives the result.

2.5 Output

The output of the currency recognisation will be given in the format of text as well as the voice.The test output will be given in a text box of GUI.

Voice Output: The most widely used „Speech synthesis‟ also called „Text to speech synthesis‟ is the artificial production of human speech. A computer system used for this purpose is called a speech synthesizer and can be implemented in software. A text-to-speech (TTS) system converts text to speech in MATLAB[12].

4. RESULTS

In this section results obtain after performing morphological image processing operation. An original image is read from database. A captured image has dimensions 866×356, vertical and horizontal revolution

96dpi. The read out image shown in Figure 4.1 titled as

“Image acquisition of 2000 rs note”.

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Figure 4.1: Image acquisition of 2000 rs note

Feature Extraction is a method of capturing visual content of images for indexing & retrieval. Primitive or low level image features can be either general features, such as extraction of color, texture and shape or domain specific features.The results of the feature extraction are calculated and compared with the stored values gives the output of the Recognisation Fig[4.2].

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Fig.[4.5]-Recognized Currency.

Haar features are extracted and stored for the comparison to give the verification result.shown in Fig[4.3],[4.4]

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Fig [4.3]-Haar features extracted.

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Fig[4.4]-Haar Wavelet

5. CONCLUSION AND FUTURE WORK

By using digital image processing, analysis of Currency image is more accurate as well as this method is efficient in terms of cost and time consuming compared to existing techniques.MATLAB Software use for this analysis.Day by day research work is increasing in this field and various image processing techniques are implemented in order to get more accurate result. The proposed system is worked effectively for extracting feature of Indian currency images.

Extracted features of currency image will be using for currency recognition with denomination as well as for its verification.Application based system shall be designed to get proper result whether currency image is fake or its genuine.

REFERENCES

[1.1] Fake currency detection using image processingTushar Agasti et al 2017 IOP Conf. Ser.: Mater. Sci. Eng. 263 052047

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