This thesis aims to implement a segmentation-free strategy in the context of handwritten multi-digit string recognition. Three models namely VGG-16, CRNN and 4C are built to be evaluated and benchmarked, also research about the effect of the different training set on model performance is carried out.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:bth-20685 |
| Date | January 2020 |
| Creators | Zhao, Mengqiao |
| Publisher | Blekinge Tekniska Högskola, Institutionen för datavetenskap |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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