Soni, KanalKanalSoni2025-08-312025-08-312023-01-01[9789811922244]10.1007/978-981-19-2225-1_282-s2.0-85140454177https://d8.irins.org/handle/IITG2025/25784Unconstrained handwritten identification is among the toughest situations in recognition and image processing. This appears to be a simple operation for an individual, however, acknowledging handwriting is a time-consuming effort for a system. In the context of a device, the entry should first be digitized from a record, a photograph, or a legitimate device such as a desktop, tablet, or laptop. The digitized text or numeral is then changed into digital form text using the Handwritten Character Identification method. This could be managed and done in two ways: online plus offline. The central target of this survey is offline authentication of Gujarati scripts (characters plus numerals) in paper and electronic materials. Numerous neural and machine learning frameworks with classification techniques were being utilized, however, the bulk of machine methodologies demonstrated efficacy in spotting these scripts in the end.falseGaussian functionality | Gujarati scripts | Machine learning frameworks | Neural frameworks | OCR—optical character recognition | Textual analysisConference Paper23673389311-31920230cpBook Series0