Method and apparatus for OCR detection of valuable documents by means of a matrix camera

Abstract

The invention relates to a method for OCR detection of valuable documents in a cash dispenser in the case of which an image of the valuable document is detected by means of a digital video or matrix camera. A Hough transformation is used to calculate edge lines of the valuable document and a rotation angle is calculated therefrom such that the edges of the valuable document are aligned with the image edges. The detected image is homogenized to compensate an inhomogeneous image background. This is followed by OCR detection of alphanumeric information on the valuable document.

Claims

1. Method for optical character recognition (OCR) detection of documents in a self-service machine, in particular an automatic teller machine or cash dispenser, comprising: a) detecting an image of a document by means of a digital video or matrix camera having a detection region; b) establishing an intermediate document image by: i) reducing the detected image, ii) determining a position of a document region, corresponding to the document and also edge pixels in the detected image, iii) detecting straight edge lines of the document region with the aid of the determined edge pixels by using a Hough transformation, iv) determining a rotation angle by which the document region in the detected image must be rotated for alignment at edges of the detection region of the digital video or matrix camera, and v) rotating the document region by the rotation angle, resulting in the intermediate document image; and c) forming a background image by: i) reducing the intermediate document image, ii) after said reducing the intermediate document image, removing smaller details from the reduced intermediate document image by filtering the document region, resulting in the background image; d) creating a brightness map of the background image; and e) binarizing the background image to segment alphanumeric character information.

2. Method according to claim 1, in which use is made in said detecting straight edge lines of an edge filter which outputs a binary edge image of the document region.

3. Method according to claim 2, in which the Hough transformation is executed with the aid of the binary edge image.

4. Method according to claim 3, in which during the Hough transformation it is determined for each pixel, which line runs therethrough, and an assessment of each line is raised when the pixel is an edge pixel, the straight edge lines corresponding to the lines most highly assessed.

5. Method according to claim 1, in which said forming the background image furthermore comprises: subtracting the brightness map from the detected intermediate document image.

6. Apparatus for optical character recognition (OCR) detection of documents in a self-service machine, in particular an automatic teller machine or cash dispenser, in particular designed as a document detection module, comprising: a digital video or matrix camera having a detection region in order to detect an image of a document; and an image processing section which is designed in order to: a) establish an intermediate document image by: i) reducing the detected image, ii) determining a position of a document region, corresponding to the document and also edge pixels in the detected image, iii) detecting straight edge lines of the document region with the aid of the determined edge pixels by using a Hough transformation, iv) determining a rotation angle by which the document region must be rotated for alignment at edges of the detection region of the digital video or matrix camera, and v) rotating the document region by the rotation angle, resulting in the intermediate document image; and c) form a background image by: i) reducing the intermediate document image, ii) removing smaller details from the reduced intermediate document image after reducing the intermediate document image by filtering the document region, resulting in the background image; and d) creating a brightness map of the background image and binarizing the background image to detect alphanumeric character information by OCR detection.

7. Apparatus according to claim 6, in which the image processing section is further designed to apply an edge filter which outputs a binary edge image of the document region.

8. Apparatus according to claim 7, in which the Hough transformation is executed with the aid of the binary edge image.

9. Apparatus according to claim 8, in which during the Hough transformation it is determined for each pixel, which line runs therethrough, and an assessment of each line is raised when the pixel is an edge pixel, the straight edge lines corresponding to the lines most highly assessed.

10. Apparatus according to one of claim 6, in which the image processing section is further designed to subtract said brightness map from the intermediate document image.

Description

OVERVIEW OF FIGURES

(1) The invention is described below in an exemplary way and with reference to the attached drawings from which further features, advantages and objects to be achieved emerge. In the drawings:

(2) FIG. 1 is a schematic of the detection of a valuable document deposited on a depositing plate by means of a video or matrix camera in a valuable document detection module in accordance with the present invention;

(3) FIG. 2 is a schematic block diagram of an image evaluation module of an inventive valuable document detection module;

(4) FIG. 3 shows a flowchart of the basic steps of an inventive method for OCR detection of valuable documents in a cash dispenser;

(5) FIG. 4 is a schematic flowchart of the steps of an automatic fine rotation for automatically aligning and cutting to size a rectangular detail as valuable document region in the acquired image;

(6) FIG. 5 is a schematic flowchart of the most important steps of a homogenization during an inventive method in preparation for image binarization; and

(7) FIGS. 6a-6l show results of diverse method steps according to the present invention with the aid of an example for the detection of a check in the so-called Bolletini check format.

DETAILED DESCRIPTION OF A PREFERRED EXEMPLARY EMBODIMENT

(8) In accordance with FIG. 1, the matrix or video camera 2 arranged above or below the depositing plate 3 acquires the valuable document 4 deposited on the depositing plate 3, the field of view 5 of the camera 2 extending up to the edges of the latter and being larger than customary valuable documents 4 to be detected. The latter are usually not aligned exactly with the edges of the depositing plate 3 but rather are tilted, a circumstance that must be taken into account. The background of such an image acquired with the aid of the camera 2 is dependent on the illumination and inhomogeneous. Depending on the camera position, moreover, perspective and radial distortions occur in the acquired image. Moreover, the resolution of the image decreases toward the edge.

(9) The camera 2 with its image sensor 13 corresponds to an image signal generator 11 of the image evaluation module 10 shown in FIG. 2. The image sensor 13 acquires a digital image of the valuable document with a predetermined resolution. The digital images thus acquired are firstly buffered in the memory 15 and subsequently processed further in an image processing section 16. The data processing section 12 of the image evaluation module 10 further comprises a central control device (CPU) 14 which is connected to a program code memory 18, in which program code instructions for executing the inventive method are stored, a control section 19, for example for presetting the image evaluation module 10, the image processing section 16, the memory 15 and an image output device 17.

(10) In accordance with FIG. 3, during a method for OCR detection of valuable documents a digital image of the valuable document is firstly acquired in step S301 and is radially corrected as required in step S302 with the aid of the physical properties of the camera objective (focal length, distortion, etc.). Moreover, it is also possible for the image also to be perspectively corrected with the aid of the camera position in relation to the edges of the depositing plate, this being, in particular, dependent on the distance of the camera from the depositing plate, and on the focal length in use, but is not mandatory.

(11) Subsequently, in step S303 the valuable document is identified and its position determined in order to identify a valuable document region, that is to say to identify pixels which correspond to the valuable document deposited on the depositing plate. In step S304, a fine rotation is then performed such that the edges of the valuable document region which is then rotated are aligned with the image edges, that is to say extend substantially parallel thereto. Subsequently in step S305 there is cut to size from the acquired image a rectangular region which corresponds to the valuable document region in which it is intended to execute OCR detection later.

(12) Subsequently, in step S306 the image background is homogenized, and after that an image is stored for later OCR analysis in step S307. The OCR analysis can be executed by means of conventional OCR algorithms which are sufficiently well known and therefore have no need to be considered further.

(13) The steps of an automatic fine rotation for aligning the acquired valuable document region are explained below with the aid of FIG. 4. In order to carry out a fine rotation, the valuable document must first be identified, and its position must be determined. To this end, it is advantageous to work on a reduced image, since it is thereby possible to attain a higher processing speed. Moreover, it is advantageous to work from a reduced image in which interfering or superfluous details such as, for example, alphanumeric characters, graphic information, or else dust filaments and interfering lines are removed. The point is that such detailed information is not needed for determining the edges and position of the valuable document. Such details can be removed by using suitable filters including, for example, the median filter on which step S402 is based and in the case of which the grayscale value of the current pixel is replaced by the median of the grayscale values of the current environment, it being possible to prescribe the size of the environment in a variable fashion, for example, via the control section 19.

(14) The valuable document region is then identified in step S403 by automatic thresholding. For example, the image processing section determines whether a pixel value is greater than a predetermined threshold or not, in order thus to binarize an edge image. The threshold can be a fixed value, or a variable which is obtained, for example, with the aid of a variable threshold method. Of course, it is also possible for this purpose to use any other desired algorithms for edge identification.

(15) In the next step S404, the edge pixels of the valuable document region are then calculated. Subsequently, in step S405 a Hough transformation is used to detect the dominant lines in the image. In the Hough method disclosed in U.S. Pat. No. 3,069,654, geometrical objects are detected by creating a dual space in which all possible parameters of the geometrical figure to be found are plotted in the dual space for each point in the image which lies on an edge. Each point in the dual space thereby corresponds to a geometrical object in the image space. When detecting straight lines by means of the Hough transformation, it is necessary firstly to find suitable parameters on a straight line, for example, slope and y-intercept or, preferably, a characterization of a straight line by its Hessian normal form. It is advantageous here that the edges in the starting image were firstly determined in step S404. During the Hough transformation, it is determined for each pixel which line (for example, as determined by angle and distance from the left-hand, upper image corner) runs through it. If the pixel under consideration is an edge pixel, the assessment of the line is raised. The most highly assessed lines then correspond to the dominant lines in the image region.

(16) These dominant lines can then be used in step S406 to easily determine the angle by which the valuable document region must be rotated in order to correct its misalignment and align it parallel to the edges of the field of view or image edges or the depositing plate. Subsequently, the image of the valuable document region is then rotated by this determined rotation angle in step S407. Subsequently, a rectangular image region which contains the valuable document region is cut out in step S408. Owing to the previously performed flush rotation, the alphanumeric characters in this region are aligned flush with the image edges in the case of the underlying rectangular original format, at any rate when an appropriate image correction has been executed previously. Precisely in the case of smaller image formats, such as occur with customary valuable documents, such image distortions are, however, not so interfering that they must necessarily be compensated. Rather, according to the invention it is possible to reliably execute an OCR detection of alphanumeric characters even when the alphanumeric characters are not exactly aligned with the image edges after step S407.

(17) FIGS. 6a-6l summarize the results of the abovenamed method steps with the aid of the practical example of the detection of a check in the so-called Bolletini check format. In accordance with FIG. 6a, the image of the check acquired by the depositing plate contains the black edge region 60 without any information and the actual valuable document region 61, which contains the graphic image information 62, letters 63, digits 64, and a barcode 65. On view are an inhomogeneous illumination of the image region and a reflection somewhat in the middle of the image, these being the result of reflections from the surface of the depositing plate.

(18) In order quickly to identify and determine the position of the valuable documents, it is preferred to work on a reduced image on which the details, such as font, dust filaments and lines on the check itself have been removed by applying a median filter, as shown in FIG. 6b. On view are Blurry details 66 and furthermore, significant image components in the regions 67, which result from the barcode and the graphic symbol on the check (compare FIG. 6a).

(19) The check is then identified by automatic thresholding (compare FIG. 6c) and an edge filter is applied to determine the edges of dominant regions, as shown in FIG. 6d, specifically the edges 68 of the valuable document, and the edges 69 of further prominent details, in particular resulting from the above-named barcode. The edges are subsequently calculated by means of a Hough transformation. These are indicated in FIG. 6e by the lines 70. It can be seen that said lines do not run parallel to the image edges. However, the rotation angle relevant here can easily be calculated from the edge image in accordance with FIG. 6e.

(20) As shown in FIG. 6a, with the aid of the rotation angle thus determined, the acquired image, that is to say the image having the full image resolution, is rotated, and the regions outside of the calculated edge lines are subsequently cut off (compare FIG. 6e), and this results in the rectangular image region shown in FIG. 6f, which contains the actual valuable document region 61, but also edge regions 60 which additionally result from unavoidable image distortions, for example, resulting from the camera objective. However, it has been shown that OCR detection can also be executed reliably even on original images thus prepared.

(21) A simple image binarization based on the image information in accordance with FIG. 6f would, however, lead to the result shown in FIG. 6g in which, for example, the text information is missing in the region of the camera owing to reflections in the middle of the image, but is still to be seen unclearly.

(22) For homogenization, a brightness map of the image background is created and then subtracted in principle from the original image in accordance with FIG. 6f. However, to further speed up the process here it is possible to reduce the image again, for example to of the original size. The result is shown in FIG. 6h. A median filter which removes the details from the image is then applied. The background image thereby resulting after the median filtering is in FIG. 6i. In the case of a median filter, a list of the value of all the neighbor pixels is created for each pixel and sorted, and the original pixel is replaced by the value found in the middle of the list. The size of the filter in this case regulates the size of this neighborhood. The filter has the property that coarse structures remain, small structures being smoothed. With relative pixel accuracy, the image generated still contains here the coarse brightness distribution on the check background. This image background is subtracted from the original image in accordance with FIG. 6f, the result being the image in accordance with FIG. 6j. Said image is inverted, and this results in the image in accordance with FIG. 6k. It is to be seen that, for example, the text information in the middle of the image is substantially easier to read and evaluate in this image. Said image is then binarized, that is to say translated into brightness values 1 or 0. The resulting starting image for the OCR detection is shown in FIG. 6l. The text can then be segmented and made available for the subsequent OCR software.

(23) In summary, the inventive method can be used to reliably execute OCR detection by means of a matrix or video camera. It may expressly be pointed out that the invention can be used in any desired self-service machine, in particular in automatic teller machines or cash dispensers whose function is to support the automatic submission of valuable documents such as, for example, checks.

LIST OF REFERENCE NUMERALS

(24) 1 Valuable document detection module

(25) 2 Video camera/matrix camera

(26) 3 Depositing plate

(27) 4 Valuable document

(28) 5 Field of view of camera 2

(29) 6 Valuable document region

(30) 10 Image evaluation module

(31) 11 Image signal generator

(32) 12 Data processing section

(33) 13 Image sensor

(34) 14 CPU

(35) 15 Memory

(36) 16 Image processing section

(37) 17 Image output device

(38) 18 Program code memory

(39) 19 Control section

(40) 60 Edge region

(41) 61 Valuable document region

(42) 62 Graphic information

(43) 63 Letters

(44) 64 Digits

(45) 65 Barcode

(46) 66 Blurry details

(47) 67 Further prominent details

(48) 68 Edge of valuable document

(49) 69 Edge of further prominent details

(50) 70 Calculated edge lines

(51) 71 Region of higher brightness