The process of evaluating students’ answers is a time consuming and effort for teachers, therefore, based on this, Grading Multiple Choice Questions (G-MCQ) is proposed to auto-marking answer without human interaction. All the human does, is to use digital camera without using expensive ordinary document scanner and machine-read for this purpose, then, evaluating and marking each correct answer is algorithm duty. G-MCQ is based on a prepared bubble sheet that contains (54) questions with four circles options for each question, G-MCO is programmed using Python programming language, passes three main process , the first one, is a preparation of scanned document, then, second one, is to detect bookmarks, First Question Bookmark (FQB), Questions Bookmarks (QB) and Options Bookmarks (OB) positions, based on detecting FQB, QB and OB, the final one is started to detect answers which are circles positions of each question from instructor. The algorithm is tested with input images with PNG and JPG format, the result of detecting of accuracy is about 99%
The process of evaluating students’ answers is a time consuming and effort for teachers, therefore, based on this, Grading Multiple Choice Questions (G-MCQ) is proposed to auto-marking answer without human interaction. All the human does, is to use digital camera without using expensive ordinary document scanner and machine-read for this purpose, then, evaluating and marking each correct answer is algorithm duty. G-MCQ is based on a prepared bubble sheet that contains (54) questions with four circles options for each question, G-MCO is programmed using Python programming language, passes three main process , the first one, is a preparation of scanned document, then, second one, is to detect bookmarks, First Question Bookmark (FQB), Questions Bookmarks (QB) and Options Bookmarks (OB) positions, based on detecting FQB, QB and OB, the final one is started to detect answers which are circles positions of each question from instructor. The algorithm is tested with input images with PNG and JPG format, the result of detecting of accuracy is about 99%
In this paper, Algorithm named (MRWL) Max Rightmost White Line is proposed to detect Kurdish/ Arabic characters’ segmentation in scanned document (printed document), it works in preprocess and segmentation stages of OCR processes, these two stages are significant parts of OCR and affects the accuracy of algorithm. The MRWL starts to remove text margins around document to reduce processing time, then, scans to find Top Line (TL) and Bottom Line (BL) for each sentence in paragraph which can be used to measure height of characters. Based on TL and BL, the Base Line (BSL) can be detected using horizontally Most Frequency Black Pixel (MFBP) which is useful to find characters’ segmentation (Atallah and Omar, 2008) . Finding TL, BL and BSL of each sentence help to find characters location in document. Six phases involve in algorithm, each phase has its own functionally. The Algorithm is tested with different input documents and the average accurate rate of detected segmentations is recorded as 96.93%.
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