Computer-implemented systems and methods are provided for identifying language that would be considered obscene or otherwise offensive to a user or proprietor of a system. A plurality of offensive words are received, where each offensive word is associated with a severity score identifying the offensiveness of that word. A string of words is received. A distance between a candidate word and each offensive word in the plurality of offensive words is calculated, and a plurality of offensiveness scores for the candidate word are calculated, each offensiveness score based on the calculated distance between the candidate word and the offensive word and the severity score of the offensive word. A determination is made as to whether the candidate word is an offender word, where the candidate word is deemed to be an offender word when the highest offensiveness score in the plurality of offensiveness scores exceeds an offensiveness threshold value.

Systems and Methods for Word Offensiveness Detection and Processing Using Weighted Dictionaries and Normalization

Augmented reality language translation system and method

Embodiments of a computer system, a method, and a computer-program product (e.g., software) for use with the computer system are described. These embodiments may be used to identify and correct errors in financial information that was extracted using character-recognition software, such as optical character recognition software and/or intelligent character recognition software. In particular, potential errors may be identified by comparing the financial information for a current financial transaction of a user with expected financial information from one or more previous financial transactions of the user. Error metrics for these potential errors may be determined and used to correct at least some of the potential errors. For example, values of the Levenshtein edit distance may be determined based on the comparison, and one or more potential errors associated with one or more minimum values of the Levenshtein edit distance may be corrected.

Technique for correcting character-recognition errors

Computer-implemented systems and methods are provided for identifying language that would be considered obscene or otherwise offensive to a user or proprietor of a system. A first plurality of offensive words are received, and a second plurality of offensive words are received. A string of words are received, where one or more detected offensive words are selected from the string of words that matches words from the first plurality of offensive words or the second plurality of offensive words. The string of words is processed based upon the detection of offensive words in the string of words.

Systems and Methods for Word Offensiveness Processing Using Aggregated Offensive Word Filters

A character recognition method is provided, including the steps of: recognizing a character string, which includes one or more characters, in a region of interest (ROI) using a first convolutional neural network (CNN) model to generate a recognized character string; and comparing the recognized character string with a plurality of character strings in a character database to find a character string corresponding to the recognized character string, wherein each of the character strings includes one or more characters; wherein if the character string corresponding to the recognized character string is found, the recognized character string is used as a character recognition result, otherwise, using a second CNN model to correct the recognized string and generate a new recognized character string as the character recognition result.

Character recognition systems and character recognition methods thereof using convolutional neural network

Methods and systems of mapping of an optical character recognition (OCR) text string to a code included in a coding dictionary by supplementing the Levenshtein Distance Algorithm (LDA) with additional information in the form of adjustments based on particular character substitutions, insertions and deletions together with weighting based on multiple alternatives for the OCR text string. In one embodiment, an OCR text string mapping method (100) includes receiving (110) an OCR text string, comparing (120) it with selected text strings from a coding dictionary, computing (130) modified Levenshtein distances associated with the comparisons by determining (140) substitution penalties, determining (150) insertion penalties, determining (160) deletion penalties and combining (170) the penalties, selecting (180) the best matching text string from the coding dictionary based on the modified Levenshtein distances, determining (190) whether a maximum threshold distance is met, and assigning (200) a code associated with the best matching text string to the OCR text string when met, and assigning (210) a null or no code when not met.

Modified levenshtein distance algorithm for coding

Dropping out of color form backgrounds from images of completed forms to obtain color form dropout images retaining only the respondent information. In one embodiment, a color form image processing method (100) includes retrieving (102) a template image, retrieving (104) a respondent image, registering (106) the images against one another to establish correspondence between pixels in the respondent and template images, dilating (108) the template image, and performing (110) a color form dropout including comparing (112) corresponding pixels in the respondent and dilated template images, and determining (114) whether to keep corresponding pixels by applying (116) a geometric solid threshold comparison to assess both color similarity and relative darkness, and removing (118) pixels from the respondent image based on such comparison.

Color form dropout using dynamic geometric solid thresholding

Electronically readable forms include radio-frequency identification (RFID) devices enabling automated processing of the forms to obtain indicated responses to questions as well as entered information. In one embodiment, an electronically readable form 10 includes a substrate 12, a plurality of response areas 14A-14G thereon, and a plurality of separate radio frequency identification (RFID) devices 20A-20G thereon that are associated with the response areas 14A-14G. The response areas 14A-14G may correspond with answers 16A-16G to questions 18A-18C. Upon appropriate indication of a response within its associated response area 14A-14G, each RFID device (20A-20G) is enabled to transmit a code 22 in response to an activation signal 30 from an RFID reader unit 32. Each code 22 is different permitting a determination of which response areas 14A-14G have responses indicated therein based on the codes 22 that are received by the RFID reader unit 32. In an alternative embodiment an appropriate indication of a response within its associated response area 14A-14G will disable its respective RFID device 20A-20G. An array of RFID devices may also be provided for a response area.

Electronically readable forms

Electronically readable forms include radio-frequency identification (RFID) devices enabling automated processing of the forms to obtain indicated responses to questions as well as entered information. In one embodiment, an electronically readable form ( 10 ) includes a substrate ( 12 ), a plurality of response areas ( 14 A- 14 G) thereon, and a plurality of separate radio frequency identification (RFID) devices ( 20 A- 20 G) thereon that are associated with the response areas ( 14 A- 14 G). The response areas ( 14 A- 14 G) may correspond with answers ( 16 A- 16 G) to questions ( 18 A- 18 C). Upon appropriate indication of a response within its associated response area ( 14 A- 14 G), each RFID device ( 20 A- 20 G) is enabled to transmit a code ( 22 ) in response to an activation signal ( 30 ) from an RFID reader unit ( 32 ). Each code ( 22 ) is different permitting a determination of which response areas ( 14 A- 14 G) have responses indicated therein based on the codes ( 22 ) that are received by the RFID reader unit ( 32 ).

Timothy O. Withum

Papers

Modified levenshtein distance algorithm for coding

Color form dropout using dynamic geometric solid thresholding

Electronically readable forms

Electronically enabled forms