India is a multi-lingual multi-script country, where a printed document which contains information in the form of texts, images, etc.; the texts part may have composed with characters and numerals of one or more scripts. So, it is necessary Identify the scripts of numerals/characters from multilingual document before feeding them to their individual script OCR systems. In this paper, the system made an attempt to recognize the script of numerals belongs to Kannada, Devanagari, and English based on structural features like water reservoir, aspect ratio, horizontal and vertical strokes. Initially, Bi-script and tri-script numerals script identification experiments are conducted on a dataset of 2100 numerals string(word), by taking 700 samples for each script and noticed average accuracy for tri-script numerals is 93.62%.
Using advanced digital technologies and photo editing software, document images, such as typed and handwritten documents, can be manipulated in a variety of ways. The most common method of document forgery is adding or removing information. As a result of the changes made to document images, there is misinformation and misbelief in document images. Forgery detection with multiple forgery operations is challenging issue. As a result, special consideration is given in this work to the ten-class problem, in which a text can be altered using multiple forgery types. The characteristics are computed using RGB color components and GLCM texture descriptors. The method is effective for distinguishing between genuine and forged document images. A classification rate of 95.8% for forged handwritten documents and 93.11% for forged printed document images are obtained respectively. The obtained results are promising and competitive with state-of- art techniques reported in the literature.
Document Images, such as typed and handwritten documents can be manipulated in various ways using many sophisticated digital technologies and photo editing software's. As a result, one can alter the text in the typed and handwritten documents that leads to degradation of quality of an image. The detection of multiple inherently altering operations in an image is a challenging issue, hence in this work a novel approach is proposed for the ten-class problem in which the alteration of a text can be accomplished through multiple operations, which all create the specific pattern. These operations are analysed with the help of image quality measures and classified using random forests classifier. The proposed approach gives a better classification accuracy rate of 94% for forged printed document images and 98.80% of forged handwritten document images, which is more promising and competitive with state of the art techniques reported in the literature.
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