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Register a new userRecognition of Hand Written Arabic Names Using Deep Learning
التعرف على الأسماء العربية المكتوبة بخط اليد بإستخدام التعلم العميق
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بناء حواسيب تستغني عن أدوات الإدخال محدودة الفضاء (مثل لوحة المفاتيح) وامتلاكها لمقدرة السمع و القراءة ظل من مجالات البحث النشطة في علوم الحاسوب , قدم فيها الباحثون عدد مقدر من الطرق و الخوارزميات لحوسبةالسمع و القراءة ضمن ما يعرف بالتعرف على الأنماطفي علوم الحاسوب. ومن بين هذه الطرق الطريقة الشمولية (Holistic approach)، التي أثبتت كفاءتها في التعرف السريع (سمعاً أو قراءة) بالإضافة إلي مفهوم التعلم العميق الذي يعتبر ثورة في مجال تعلم الآلة في الوقت الحالي,وزاد الإهتمام به حديثاً خصوصاً بعد الزيادة الكبيرة في سرعة المعالجة الحاسوبية و التقدم في المعالجة المتوازية. هذه الدراسة تقدم تجارب إدراك ناجحة للشبكات العصبية العميقة في التعرف شمولياً على الأسماء العربية الأكثر شيوعاً، حيث تم إستخدام أدوات التعلم العميق و تمت تجربتها على السبعة أسماءالاكثر شيوعا بحسب مجموعة بيانات جامعة السودان للاسماء (SUST-ARG names) وبعد إجراء مراحل التدريب الخمسة , إستطاعت الشبكة أن تتعرف علي كل الأسماء وبنسبة 100% .
Designing Computerized Systems which posses reading and hearing faculties is an active research area for more than four decades. Many methods and algorithms have been suggested by researches for this purpose as part of pattern recognition research. Recently, more research work has been devoted to the holist approach the recognition system recognizes a complete word as one object without going through the long and erroneous character segmentation process. In this paper, a convolutional neural network has been designed to recognize the popular Arabic names holistically. SUSt ARG names data set has been used to test the network performance (collected and compiled by pattern recognition research in Sudan University of Science and Technology-SUSt). Selecting an appropriate deep learning toolbox, after five stages of training, the network was able to recognize all the names and 100%
Artificial intelligence review:
Research summary
البحث المقدم من جامعة السودان للعلوم والتكنولوجيا يركز على تصميم نظام للتعرف على الأسماء العربية المكتوبة بخط اليد باستخدام تقنيات التعلم العميق، وتحديداً الشبكات العصبية الالتفافية. تمت تجربة النظام على مجموعة بيانات SUST-ARG التي تحتوي على أسماء عربية شائعة. بعد خمس مراحل من التدريب، تمكنت الشبكة العصبية من تحقيق نسبة دقة تصل إلى 100% في التعرف على الأسماء. البحث يتناول أيضاً مراحل معالجة الصور الرقمية، بدءاً من الإعداد المسبق للصور، مروراً بإزالة الشوائب وتوحيد الأحجام، وصولاً إلى مرحلة التعرف باستخدام الشبكة العصبية. النتائج أظهرت فعالية النظام في التعرف على الأسماء بدقة عالية، مما يعزز إمكانية استخدامه في تطبيقات عملية متنوعة.
Critical review
دراسة نقدية: البحث قدم إسهاماً مهماً في مجال التعرف على النصوص العربية المكتوبة بخط اليد باستخدام تقنيات التعلم العميق. ومع ذلك، هناك بعض النقاط التي يمكن تحسينها. أولاً، التركيز على مجموعة بيانات محدودة قد يقلل من تعميم النتائج على نطاق أوسع من الأسماء والنصوص. ثانياً، لم يتم التطرق بشكل كافٍ إلى التحديات التي قد تواجه النظام في التعرف على خط اليد غير المقروء أو المتداخل. ثالثاً، يمكن تحسين البحث بإضافة مقارنات مع تقنيات أخرى للتعرف على النصوص لمعرفة مدى تفوق النظام المقترح.
Questions related to the research
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ما هي التقنية الرئيسية المستخدمة في البحث للتعرف على الأسماء العربية المكتوبة بخط اليد؟
التقنية الرئيسية المستخدمة هي الشبكات العصبية الالتفافية (Convolutional Neural Networks).
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ما هي مجموعة البيانات التي تم استخدامها لاختبار أداء الشبكة العصبية؟
تم استخدام مجموعة بيانات SUST-ARG التي تحتوي على أسماء عربية شائعة.
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ما هي نسبة الدقة التي حققها النظام في التعرف على الأسماء بعد التدريب؟
النظام حقق نسبة دقة تصل إلى 100% في التعرف على الأسماء.
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ما هي المراحل التي تمر بها الصور قبل إدخالها إلى الشبكة العصبية للتعرف عليها؟
المراحل تشمل الإعداد المسبق للصور، إزالة الشوائب، توحيد الأحجام، وتحويل الصور إلى مصفوفات يمكن معالجتها بواسطة الشبكة العصبية.
References used
Li Deng and Dong Yu (2014), "Deep Learning: Methods and Applications", Foundations and Trends® in Signal Processing
Ian Goodfellow, Yoshua Bengio, and Aaron Courville (2016): Deep Learning. MIT Press
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