| Project/Area Number |
11680403
|
|---|
| Research Category |
Grant-in-Aid for Scientific Research (C)
|
| Allocation Type | Single-year Grants |
|---|
| Section | 一般 |
|---|
| Research Field |
Intelligent informatics
|
|---|
| Research Institution | OSAKA ELECTRO-COMMUNICATION UNIVERSITY |
|---|
Principal Investigator |
UMEDA Michio OSAKA ELECTRO-COMMUNICATION UNIVERSITY, PROFESSOR, 総合情報学部, 教授 (30213490)
|
|---|
| Co-Investigator(Kenkyū-buntansha) |
MASTUO Ken-ichi NARA NATIONAL COLLEGE OF TECHNOLOGY, ASSISTANT PROFESSOR, 情報工学科, 助教授
|
|---|
| Project Period (FY) |
1999 – 2001
|
| Project Status |
Completed (Fiscal Year 2001)
|
| Budget Amount *help |
¥2,400,000 (Direct Cost: ¥2,400,000)
Fiscal Year 2001: ¥500,000 (Direct Cost: ¥500,000)
Fiscal Year 2000: ¥500,000 (Direct Cost: ¥500,000)
Fiscal Year 1999: ¥1,400,000 (Direct Cost: ¥1,400,000)
|
|---|
| Keywords | Character Spotting / Scene Analysis / Image Processing / Character String Extraction / Character Segmentation / Character Recognition / Neural Network / Morphology / 知的ロボット / ハフ変換 |
|---|
| Research Abstract |
In the development of the character spotting technique, we picked up the city scene images which contain the character string information such as bank signboard and proposed the extraction and recognition method of these characters
As an extraction method of character string, we proposed the dynamic adaptive threshold method based on the connectivity number of picture elements and established the application method to the local target region to perform the high precision. This method is as follows ; the candidate signboared region is extracted from the scene image by using the technique of Hough transform and high performance extraction of character string area is performed by applying the adaptive threshold method ot the local region as a target area. Furthermore applying the mathematical morphology operation to the extracted local area and settling the threshold value based on the pattern spectrum analysis, it is clarified that the highly precise extraction of character string is performed and stable character spotting to rather complex scene images is possible
In the character recognition process, we proposed the highly precise recognition method by using the autoassociative neural network which learns the shape feature of every character category and applying the network to the character information contained in the bank signboard. This recognition method will be expanded to the object oriented character recognition system
On the other hand, in the implementation ot the intelligent robots basic control method and some established techniques are implemented to the robots controlled by the radio information
|
