مشكلة البحث:

يبقى الفن ولا زال في جميع حالات التعاقب البشري ومراحل التطور الانساني الاكثر انطلاقة وتميزاً في التعبير عن واقع الانسان وعن مشاعره وانفعالاته وافكاره ذات الصلة بتأثيرات البيئة المحيطة به.

والفن ولاسيما (الرسم) يمثل وسيلة من وسائل التعبير الفني بل يكاد يكون الرسم وسيلة الانسان الاولى التي عبر فيها بخطوط مرئية عن مجالات حياته وعلاقته بالبيئة التي عاش فيها، ويقيناً ان الخطوط هذه وكما شكلتها يد الانسان وظهرت بصيغة رموز وصور ذات دلالات او معانٍ عبرت عن انفعالاته واهتماماته وحالته النفسية الداخلية من جهة، وعلاقته بالبيئة التي يعشيها بجميع ظواهرها الاجتماعية والطبيعية من جهة اخرى، في اشكال ظهرت في البداية على جدران الكهوف والقصور والمعابد والمنازل التي استخدمها كاوراق للرسم عليها وفيما تركه ذلك من ادلة على التعبير الفني لمكونات بيئته.

ومع ان وسائل الرسم وطرائق التعبير عنه قد تعددت وتنوعت عبر الاف السنين الا ان ميزة التفرد الرئيسة للرسم ظلت وستبقى تمثل حاجة نفسية يلجأ اليها الانسان بوصفها الوسيلة الاقرب وضوحاً الى نفسه والاعمق اثراً في وجدانه، كون ان الرسم من اكثر الفنون تماساً بملامح حياته ومن اشدها تأثيراً بشعوره وادراكه بدليل ان الحضارات الانسانية سعت ولقرون عديدة الى ان تعبر عن تجربتها الانسانية بالرسوم التي ينجزها ابناؤها.

ان هذا التوجه يعزز القناعة بفاعلية تلك الرسوم وفي تهيئة ذلك الترابط بين الانسان وما يعتمل في نفسه وفي كل ما في بيئته من مظاهر واشكال وما تحتويه من مشاهد تتمثل في رسومه وفي رؤيته ونظراته الى البيئة وتذوق مظاهرها وما يحسه من اشكال الشخوص والكائنات الحيوانية والنباتية والحوادث التي يريد التعبير عنها.

ويشير (هربرت ريد – 1975) بهذا الصدد الى ان "الفن استعداد متوافر لدى الافراد بدرجات متفاوتة ويطبقونه في كل ما يقع تحت احاسيسهم وعواطفهم ومشاعرهم في حياتهم اليومية، وهو يكشف القيم الجمالية، فبواسطته نستطيع ان نتعرف على مكامن الجمال والقبح والتمييز بينهما في الاشياء التي تقع بالبيئة المحيطة بنا، وكذلك فانه ينمي الذوق والاحساس بالجمال وينمي الوجدان كونه متغلغل في حياة الانسان منذ القدم اذ اصبحت كل الاشياء التي يصنفها الانسان خاضعة الى حد كبير للتشكيل الفني.

 

 

Offline handwritten signature is a type of behavioral biometric-based on an image. Its problem is the accuracy of the verification because once an individual signs, he/she seldom signs the same signature. This is referred to as intra-user variability. This research aims to improve the recognition accuracy of the offline signature. The proposed method is presented by using both signature length normalization and histogram orientation gradient (HOG) for the reason of accuracy improving. In terms of verification, a deep-learning technique using a convolution neural network (CNN) is exploited for building the reference model for a future prediction. Experiments are conducted by utilizing 4,000 genuine as well as 2,000 skilled forged signatu

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Abstract

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Abstract

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Abstract:

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