Develop an offline handwriting recognition engine that can automatically read handwritten prescriptions in English from scanned or captured images. The perfect combination of a deep learning neural network and a deep learning neural network will make it possible to process the content of documents with even greater accuracy and clarity. Text can be extracted with high accuracy regardless of the quality of the original document, whether printed text, handwriting, or low-quality images.
The project aims to develop an engine that can automatically detect & remove rubber stamp from scanned/captured document images.. There are many challenging that we have to cope with in this project. For example, no any standards for rubbers so far (e.g. the variety of rubber shapes, colors) or especially dealing with both scanned and captured images are also a big challenging. After trying several methods and considering between two important metrics (accuracy and performance), we finally deployed YOLOv3 for object detection step and making use of K-means scikit-learn and OpenCV for output generation.
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