On-line Cursive Handwriting Recognition
{'Code': '15030000', 'Directorate': {'Abbreviation': 'TIP', 'LongName': 'Dir for Tech, Innovation, & Partnerships'}, 'Division': {'Abbreviation': 'TI', 'LongName': 'Translational Impacts'}}
{'SignBlockName': 'Roland T. Tibbetts', 'PO_EMAI': None, 'PO_PHON': None}
There is a widely recognized need for improved user                        interfaces that will make computers easier to use for people                    with no technical training, and that will improve the general                   efficiency and ergonomics of many computer applications where                   the conventional keyboard is too limited.  One promising                        approach is based on handwriting and gesture recognition, which                 will enable people to use common handwriting skills to                          communicate with computers for a wide range of applications                     such as text edition, form filling, and interactive graphics.                   But what is needed before handwriting/gesture interface can                     become widely popular is accurate and reliable recognition of                   normal cursive handwriting.  The Phase I study examined a new                   cursive handwriting recognition system consisting of a front-                   end Dynamic Programming (DP) algorithm, extensions to the DP                    algorithm to find the vest match and all plausible                              alternatives, dictionary matching, rules-based discrimination                   to resolve "confusions," and syntax constraints.  Prospects for                 eventual commercialization were judged to be very promising                     based on tests with a data base of cursive words which resulted                 in 99.2% correct recognition in user-independent mode, and                      99.9% in user-dependent mode.  Phase II will perform the basic                  research needed to fully develop the recognition system for                     eventual commercialization.  This includes collecting a                         comprehensive data base of cursive handwriting, further                         algorithm research and testing, human factors design, and                       development of a real-time engineering prototype.
{'FirstName': 'John', 'LastName': 'Ostrem', 'PI_MID_INIT': 'S', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'John S Ostrem', 'EmailAddress': 'ostrem@cic.com', 'NSF_ID': '000457421', 'StartDate': '11/01/1989', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
{'Name': 'Communication Intelligence Corporation', 'CityName': 'SAN FRANCISCO', 'ZipCode': '941271504', 'PhoneNumber': '4158027888', 'StreetAddress': '38 SAN FERNANDO WAY', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'California', 'StateCode': 'CA', 'CONGRESSDISTRICT': '11', 'CONGRESS_DISTRICT_ORG': 'CA11', 'ORG_UEI_NUM': 'E5R1UMLCVKR1', 'ORG_LGL_BUS_NAME': 'ISIGN SOLUTIONS INC', 'ORG_PRNT_UEI_NUM': None}
{'Name': None, 'CityName': None, 'StateCode': None, 'ZipCode': None, 'StreetAddress': None, 'CountryCode': None, 'CountryName': None, 'StateName': None, 'CountryFlag': None, 'CONGRESSDISTRICT': None, 'CONGRESS_DISTRICT_PERF': '""'}
{'Code': '5373', 'Text': 'SBIR Phase II'}
{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=1988&All=true', 'xml': '8821869.xml'}