Testing Scoring System and Method

• US 20080311551A1
• Filed: 08/23/2005
• Published: 12/18/2008
• Est. Priority Date: 08/23/2005
• Status: Abandoned Application

• Current Assignee: The Mazer Corporation
• Original Assignee: The Mazer Corporation
• Inventors: Reed, Michael Allen
• Application Number:
  US12/064,379
• Publication Number:
  US 20080311551A1
• Time in Patent Office: Days
• US Class Current: 434/356
• CPC Class Codes: G06V 30/10 Character recognition
• CPC Class Codes: G06V 30/1448 based on markings or identifiers characterising the document or the area
• CPC Class Codes: G06V 30/19013 Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching specially adapted for image segmentation G06T7/10; specially adapted for the analysis of motion G06T7/20; specially adapted for image alignment G06T7/30; specially adapted for the calculation of depth from stereo images G06T7/50; specially adapted for position determination G06T7/70

Abstract

Systems (FIG. 2) and methods for assessment of constructed responses provided in digital images are disclosed. In particular, what is disclosed are digital scoring systems (FIG. 2) and methods tehreof used in obtaining information from groups of people having the individuals (respondents) fill in pre-printed mark read forms (36) by placing marks in selected boxes on the forms and which are the scanned and analyzed by software utilizing optical mark reading (OMR) and optical/intelligent character recognition (OCR/ICR) routines.

Claims

1. A method, performed by a processor-based machine on an input image, for deskewing, cropping, and scoring a digital input image, the method comprising operating the processor-based machine to perform the steps of:

searching for a pair of grouped pixel objects in said digital input image;

determining a skew angle between coordinate locations of said pair of grouped pixel objects in the input images;

rotating the digital input image a negative amount of said skew angle;

searching for coordinate location of said one mark read field;

comparing said coordinate location of said one mark read field in said digital input image to same coordinate location of a corresponding mark read field in a correct answer digital image read from said memory;

measuring difference between said one mark read field of said digital input image and said corresponding mark read field of said correct answer digital image to determine whether a mark is in the one mark read field of said digital input image; and

providing a result of said measuring.

2. The method of claim 1, wherein said digital input image is received from a remote source.

3. The method of claim 1 further comprises selecting digitally a zone in a deskewed template digital image, and designating number and type of mark read fields in said digitally selected zone.

4. The method of claim 1 further comprises selecting digitally a zone in a deskewed template digital image, designating number and type of mark read fields in said digitally selected zone, said selecting and designating providing coordinate locations of said zone and said mark read fields therein, and saving in said memory the coordinate locations of said selected zone and mark read fields, and said number and type of mark sense fields therein.

5. The method of claim 1 further comprises deskewing said correct answer digital image before said comparison.

6. The method of claim 1 further comprises deskewing said correct answer digital image before said comparison, selecting digitally a zone in a deskewed template digital image, and designating number and type of mark read fields in said digitally selected zone, said selecting and designating providing coordinate locations of said zone and said mark read fields therein in said template digital image, and locating coordinate locations of corresponding mark-sense fields in said correct answer digital image using said coordinate locations for said zones and mark sense fields of said template digital images.

7. The method of claim 1 further comprises reading pixel values of each corresponding mark-sense field in said correct answer digital image and storing said pixel values in a first temporary file in said memory.

8. The method of claim 1 further comprises reading pixel values of each corresponding mark-sense field in said correct answer digital image, storing said pixel values in a first temporary file in said memory, reading pixel values of said one mark sense field in said input digital image, and storing said pixel values of the input digital image in a second temporary file in said memory, wherein said temporary files are compared with one another to determined whether said mark is in the one mark read field of said digital input image.

9. The method of claim 1 further comprises selecting digitally a zone in a deskewed template digital image, designating number and type of mark read fields in said digitally selected zone, said selecting and designating providing coordinate locations of said zone and said mark read fields therein in said template digital image, and locating coordinate locations of corresponding mark-sense fields in said input digital image using said coordinate locations for said zones and mark sense fields of said template digital images.

10. The method of claim 1 further comprising deskewing said correct answer digital image before said comparison, selecting digitally a zone in a deskewed template digital image, designating number and type of mark read fields in said digitally selected zone, said selecting and designating providing coordinate locations of said zone and said mark read fields therein in said template digital image, and locating coordinate locations of corresponding mark-sense fields in said input digital image and said correct answer digital image using said coordinate locations for said zones and mark sense fields of said template digital images, such that corresponding mark sense fields in said input digital image and said correct answer digital image, which have both been deskewed, have the same coordinate locations.

11. The method of claim 1 further comprises deskewing said correct answer digital image before said comparison, selecting digitally a zone in a deskewed template digital image, designating number and type of mark read fields in said digitally selected zone, said selecting and designating providing coordinate locations of said zone and said mark read fields therein in said template digital image, and locating coordinate locations of corresponding mark-sense fields in said input digital image and said correct answer digital image using said coordinate locations for said zones and mark sense fields of said template digital images, such that corresponding mark sense fields in said input digital image and said correct answer digital image, which have both been deskewed, have the same coordinate locations, and wherein said method further comprises reading pixel values of each corresponding mark-sense field in said correct answer digital image, storing said pixel values in a first temporary file in said memory, reading pixel values of said one mark sense field in said input digital image, and storing said pixel values of the input digital image in a second temporary file in said memory, wherein said temporary files are compared with one another to determined whether said mark is in the one mark read field of said digital input image.

12. The method of claim 1 further comprises reading pixel values of each corresponding mark-sense field in said correct answer digital image, storing said pixel values in a first temporary file in said memory, reading pixel values of said one mark sense field in said input digital image, and storing said pixel values of the input digital image in a second temporary file in said memory, and comparing said temporary files with one another to determined whether a correct mark in a corresponding mark sense field of the correct answer digital image matches said mark if provided in the one mark read field of said digital input image, wherein a match is indicated if the pixel value for the corresponding mark sense field in each temporary file falls within a predetermined threshold value range.

13. The method of claim 1 further comprises reading pixel values of each corresponding mark-sense field in said correct answer digital image, storing said pixel values in a first temporary file in said memory, reading pixel values of said one mark sense field in said input digital image, and storing said pixel values of the input digital image in a second temporary file in said memory, wherein said temporary files are compared with one another to determined whether said mark is in the one mark read field of said digital input image, and applying scoring rules to resolve slight differences.

14. The method of claim 1 further comprises reading pixel values of each corresponding mark-sense field in said correct answer digital image, storing said pixel values in a first temporary file in said memory, reading pixel values of said one mark sense field in said input digital image, and storing said pixel values of the input digital image in a second temporary file in said memory, wherein said temporary files are compared with one another to determined whether said mark is in the one mark read field of said digital input image, and applying scoring rules to resolve slight differences, said scoring rules handling instances of the person marking more than said one mark sense field, and not completely marking said one mark sense field.

15. The method of claim 1 further comprises cropping said input image and said correct answer digital image to remove white spaces.

16. The method of claim 1 further comprises cropping said input image and said correct answer digital image to remove white spaces using a probability distribution function.

17. The method of claim 1, wherein said providing said result of said measuring includes statistical analysis preformed on said results.

18. The method of claim 1, wherein said providing said result of said measuring includes statistical analysis preformed on said results, said statically analysis includes determining mean, variance, standard deviation, standard error, minimum, maximum, and range from said results, and generating an exportable report thereof.

19. The method of claim 1, wherein said grouped pixel objects is a Gaussian mask.

20. The method of claim 1 further comprises selecting digitally a zone in a deskewed template digital image, designating number and type of mark read fields in said digitally selected zone, wherein said selecting and designating providing coordinate locations of said zone and said mark read fields therein, saving in said memory the coordinate locations of said selected zone and mark read fields and said number and type of mark sense fields therein, wherein said designating the type and number of mark-sense field provided in the selected zones is automated by said processor applying in a routine a number of predefined digital masks of different types of mark sense fields for object identification.

44. A system for performing the method of claim 1.

21. A method of preparing and reading pre-printed forms carrying written material together with at least one mark read field within which a mark may be entered by a person who processes the document for the purpose of alternatively marking said mark read field or leaving said mark read field free of any mark, and wherein the reading of the form identifies whether said one mark read field has been marked;

said method comprising;

forming the pre-printed form by placing characters on a sheet together with said at least one mark read field for receiving a mark;

scanning the pre-printed form with an optical scanner to create a digital input image after the person may have marked said one mark read field;

processing the digital input image with a programmed machine processor having assess to memory, said processing includes said processor;

searching for a pair of grouped pixel objects in said digital input image;

determining a skew angle between coordinate locations of said pair of grouped pixel objects in the input images;

rotating the digital input image a negative amount of said skew angle;

searching for coordinate location of said one mark read field;

comparing said coordinate location of said one mark read field in said digital input image to same coordinate location of a corresponding mark read field in a correct answer digital image read from said memory;

measuring difference between said one mark read field of said digital input image and said corresponding mark read field of said correct answer digital image to determine whether a mark is in the one mark read field of said digital input image; and

providing a result of said measuring.

22. The method of claim 21, wherein said forming the pre-printing form includes using an offset web press.

23. The method of claim 21, wherein forming said pre-printed form includes printing on a web at least 16 pages per predefined length.

24. The method of claim 21 further comprising providing said pre-printed form in a booklet to the person.

25. The method of claim 21, wherein said scanning is from using a fixed head scanner without precise registration.

26. The method of claim 21, wherein said processing further includes selecting digitally a zone in a deskewed template digital image, and designating number and type of mark read fields in said digitally selected zone.

27. The method of claim 21, wherein said method further includes selecting digitally a zone in a deskewed template digital image, designating number and type of mark read fields in said digitally selected zone, said selecting and designating providing coordinate locations of said zone and said mark read fields therein, and said processing further includes saving in said memory the coordinate locations of said selected zone and mark read fields, and said number and type of mark sense fields therein.

28. The method of claim 21 wherein said processing includes deskewing said correct answer digital image before said comparison.

29. The method of claim 21, wherein said processing includes deskewing said correct answer digital image before said comparison, selecting digitally a zone in a deskewed template digital image, and designating number and type of mark read fields in said digitally selected zone, said selecting and designating providing coordinate locations of said zone and said mark read fields therein in said template digital image, and locating coordinate locations of corresponding mark-sense fields in said correct answer digital image using said coordinate locations for said zones and mark sense fields of said template digital images.

30. The method of claim 21, wherein said processing includes reading pixel values of each corresponding mark-sense field in said correct answer digital image and storing said pixel values in a first temporary file in said memory.

31. The method of claim 21, wherein said processing includes reading pixel values of each corresponding mark-sense field in said correct answer digital image, storing said pixel values in a first temporary file in said memory, reading pixel values of said one mark sense field in said input digital image, and storing said pixel values of the input digital image in a second temporary file in said memory, wherein said temporary files are compared with one another to determined whether said mark is in the one mark read field of said digital input image.

32. The method of claim 21, wherein said processing includes selecting digitally a zone in a deskewed template digital image, and designating number and type of mark read fields in said digitally selected zone, said selecting and designating providing coordinate locations of said zone and said mark read fields therein in said template digital image, and locating coordinate locations of corresponding mark-sense fields in said input digital image using said coordinate locations for said zones and mark sense fields of said template digital images.

33. The method of claim 21, wherein said processing includes deskewing said correct answer digital image before said comparison, selecting digitally a zone in a deskewed template digital image, and designating number and type of mark read fields in said digitally selected zone, said selecting and designating providing coordinate locations of said zone and said mark read fields therein in said template digital image, and locating coordinate locations of corresponding mark-sense fields in said input digital image and said correct answer digital image using said coordinate locations for said zones and mark sense fields of said template digital images, such that corresponding mark sense fields in said input digital image and said correct answer digital image, which have both been deskewed, have the same coordinate locations.

34. The method of claim 21, wherein said processing includes deskewing said correct answer digital image before said comparison, selecting digitally a zone in a deskewed template digital image, and designating number and type of mark read fields in said digitally selected zone, said selecting and designating providing coordinate locations of said zone and said mark read fields therein in said template digital image, and locating coordinate locations of corresponding mark-sense fields in said input digital image and said correct answer digital image using said coordinate locations for said zones and mark sense fields of said template digital images, such that corresponding mark sense fields in said input digital image and said correct answer digital image, which have both been deskewed, have the same coordinate locations, and wherein said processing includes reading pixel values of each corresponding mark-sense field in said correct answer digital image, storing said pixel values in a first temporary file in said memory, reading pixel values of said one mark sense field in said input digital image, and storing said pixel values of the input digital image in a second temporary file in said memory, wherein said temporary files are compared with one another to determined whether said mark is in the one mark read field of said digital input image.

35. The method of claim 21, wherein said processing includes reading pixel values of each corresponding mark-sense field in said correct answer digital image, storing said pixel values in a first temporary file in said memory, reading pixel values of said one mark sense field in said input digital image, and storing said pixel values of the input digital image in a second temporary file in said memory, and comparing said temporary files with one another to determined whether a correct mark in a corresponding mark sense field of the correct answer digital image matches said mark if provided in the one mark read field of said digital input image, wherein a match is indicated if the pixel value for the corresponding mark sense field in each temporary file falls within a predetermined threshold value range.

36. The method of claim 21, wherein said processing includes reading pixel values of each corresponding mark-sense field in said correct answer digital image, storing said pixel values in a first temporary file in said memory, reading pixel values of said one mark sense field in said input digital image, and storing said pixel values of the input digital image in a second temporary file in said memory, wherein said temporary files are compared with one another to determined whether said mark is in the one mark read field of said digital input image, and applying scoring rules to resolve slight differences.

37. The method of claim 21, wherein said processing includes reading pixel values of each corresponding mark-sense field in said correct answer digital image, storing said pixel values in a first temporary file in said memory, reading pixel values of said one mark sense field in said input digital image, and storing said pixel values of the input digital image in a second temporary file in said memory, wherein said temporary files are compared with one another to determined whether said mark is in the one mark read field of said digital input image, and applying scoring rules to resolve slight differences, said scoring rules handling instances of the person marking more than said one mark sense field, and not completely marking said one mark sense field.

38. The method of claim 21, wherein said processing further includes cropping said input image and said correct answer digital image to remove white spaces.

39. The method of claim 21, wherein said processing further includes cropping said input image and said correct answer digital image to remove white spaces using a.

40. The method of claim 21, wherein said providing said result of said measuring includes statistical analysis preformed on said results.

41. The method of claim 21, wherein said providing said result of said measuring includes statistical analysis preformed on said results, said statically analysis includes determining mean, variance, standard deviation, standard error, minimum, maximum, and range from said results, and generating an exportable report thereof.

42. The method of claim 21, wherein said grouped pixel objects is a Gaussian mask.

43. The method of claim 21, wherein said processing further includes selecting digitally a zone in a deskewed template digital image, designating number and type of mark read fields in said digitally selected zone, wherein said selecting and designating providing coordinate locations of said zone and said mark read fields therein, said processing further includes saving in said memory the coordinate locations of said selected zone and mark read fields, and said number and type of mark sense fields therein, wherein said designating the type and number of mark-sense field provided in the selected zones is automated by said processor applying in a routine a number of predefined digital masks of different types of mark sense fields for object identification.

45. A system for performing the method of claim 21.