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Register a new userLayoutReader: Pre-training of Text and Layout for Reading Order Detection
Layoutreader: ما قبل التدريب للنص والتخطيط لكشف عن ترتيب القراءة
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كشف ترتيب القراءة هو حجر الزاوية لفهم المستندات البصرية (على سبيل المثال، الإيصالات والأشكال). لسوء الحظ، أي عمل موجود استفاد من نماذج التعلم العميقة المتقدمة لأنها شاقة للغاية للتعليق على مجموعة بيانات كبيرة بما فيه الكفاية. نلاحظ أن ترتيب القراءة من مستندات Word مضمن في بيانات تعريف XML الخاصة بهم؛ وفي الوقت نفسه، من السهل تحويل مستندات Word إلى ملفات PDF أو الصور. لذلك، في طريقة تلقائية، نقوم ببناء Redlybank، مجموعة بيانات معيار تحتوي على ترتيب القراءة والنصوص والتخطيط لمعلومات 500000 صورة وثيقة تغطي مجموعة واسعة من أنواع المستندات. هذه مجموعة بيانات كبيرة على نطاق واسع يطلق تشغيل قوة الشبكات العصبية العميقة لكشف عن الطلب. على وجه التحديد، يلتقط LayoTreader المقترح معلومات النص والتخطيط لتوقعات ترتيب القراءة باستخدام نموذج SEQ2SEQ. يؤدي ذلك بشكل مثالي تقريبا في اكتشاف أمر القراءة ويحسن بشكل كبير من محركات الحرية الخارجية المفتوحة والمصادر الحرارية في ترتيب خطوط نصية في نتائجها في تجاربنا. يتم توفير مجموعة البيانات والنماذج علنا في https://aka.ms/layouoTreader.
Reading order detection is the cornerstone to understanding visually-rich documents (e.g., receipts and forms). Unfortunately, no existing work took advantage of advanced deep learning models because it is too laborious to annotate a large enough dataset. We observe that the reading order of WORD documents is embedded in their XML metadata; meanwhile, it is easy to convert WORD documents to PDFs or images. Therefore, in an automated manner, we construct ReadingBank, a benchmark dataset that contains reading order, text, and layout information for 500,000 document images covering a wide spectrum of document types. This first-ever large-scale dataset unleashes the power of deep neural networks for reading order detection. Specifically, our proposed LayoutReader captures the text and layout information for reading order prediction using the seq2seq model. It performs almost perfectly in reading order detection and significantly improves both open-source and commercial OCR engines in ordering text lines in their results in our experiments. The dataset and models are publicly available at https://aka.ms/layoutreader.
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