Calibration of blurred text in images using deep learning techniques (Record no. 6569)
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| 000 -LEADER | |
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| fixed length control field | 01515nam a22002177a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20220107122853.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 190711b xxu||||| |||| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE | |
| Transcribing agency | IIITMK |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Nayana Uday (41718006) |
| 9 (RLIN) | 16168 |
| 245 ## - TITLE STATEMENT | |
| Title | Calibration of blurred text in images using deep learning techniques |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | MPhil CS 2018-2019 |
| 500 ## - GENERAL NOTE | |
| General note | Text in an image carries a high level of information for humanity. These texts have an<br/>important role in the computer vision application. In recent years, the rapid development<br/>of machine learning and deep learning, in<br/>uence the applications of computer vision and<br/>document analysis topics. All the previously proposed methods use dierent algorithms<br/>to detect text in images; however, they suer from poor performance while performing<br/>detection in blurred images. The proposed method capable of handling blurred text<br/>detection and recognition in images. It is an automatic end to end system to recognize the<br/>blurred text in images. It has two stages, rst is the detection of text in an image using<br/>an object detection method. In the second step, it segments the text area and recognizes<br/>it using hybrid CNN and LSTM method. It acquires 92% accuracy in detection and 93%<br/>accuracy in the recognition phase. |
| 502 ## - DISSERTATION NOTE | |
| Degree type | MPhil CS |
| Name of granting institution | 2018-2019 |
| Year degree granted | INT |
| -- | Dr Elizabeth Sherly |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | BLURRED TEXT |
| 9 (RLIN) | 16169 |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | DEEP LEARNING |
| 9 (RLIN) | 16170 |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | OBJECT DETECTION |
| 9 (RLIN) | 16171 |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | CNN |
| 9 (RLIN) | 16172 |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | LSTM |
| 9 (RLIN) | 16173 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Collection code | Home library | Current library | Shelving location | Date acquired | Total Checkouts | Barcode | Date last seen | Price effective from | Koha item type |
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| Dewey Decimal Classification | Non Fiction | IIITM-K | Kerala University of Digital Sciences, Innovation and Technology Knowledge Centre | 11/07/2019 | R-1573 | 11/07/2019 | 11/07/2019 | Project Reports |