Robo-Advisors to Predict Switching of Jobs using Space in Handwriting Image
Sudiksha Chakraborty1, Jyoti Majumder2
1Sudiksha Chakraborty, Department of Finance, IBS Kolkata, Kolkata (West Bengal), India.
2Jyoti Majumder, Department of Marketing, IBS Kolkata, Kolkata (West Bengal), India.
Manuscript received on 10 September 2019 | Revised Manuscript received on 19 September 2019 | Manuscript Published on 11 October 2019 | PP: 459-465 | Volume-8 Issue-11S September 2019 | Retrieval Number: K107809811S19/2019©BEIESP | DOI: 10.35940/ijitee.K1078.09811S19
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Predicting switching of jobs has been in quest for several years. The study starts with collection of manually written text specimen in A4 sheet. At the beginning gray scale or color copy is extracted by the robo-advisor and more shielding executed to transform to binary image. For identifying the gaps amidst characters Skew-normalization is used in the manuscript after segmentation. After that a comparison is computed with space mean among closed loops created by characters and word spaces to identify character. The characters are then matched with the requirements of job. Accordingly, the new appraisal is compared against the already existing. If the performance degrades, switching is predicted along with the possible job options. The main agenda of the document is to analyze switching jobs on the basis of behavior from gaps in manually written manuscripts. The recommended approach is approved with 600 samples of IAM database with diverse authors having various culture. The analysis concludes the recommended method attains 64% and above level of efficiency.
Keywords: Space Analysis; Character Analysis; Business Requirements; Performance Analysis; Switching of Jobs.
Scope of the Article: Routing, Switching and Addressing Techniques