Deep learning for graphics recognition: document understanding and beyond
نویسندگان
چکیده
منابع مشابه
Document image processing: graphics recognition
Document analysis or processing is mainly related to texts and graphics. It concerns separation, localisation and recognition. According to Nagy (2000), document analysis is related to document image analysis (DIA) since the overall research works have been concerned with document image interpretation. In a similar manner, Kasturi et al. (2002) categorises document image analysis into two domai...
متن کاملNamed Entity Recognition in Persian Text using Deep Learning
Named entities recognition is a fundamental task in the field of natural language processing. It is also known as a subset of information extraction. The process of recognizing named entities aims at finding proper nouns in the text and classifying them into predetermined classes such as names of people, organizations, and places. In this paper, we propose a named entity recognizer which benefi...
متن کاملLearning for Deep Language Understanding
The paper addresses the problem of learning to parse sentences to logical representations of their underlying meaning, by inducing a syntacticsemantic grammar. The approach uses a class of grammars which has been proven to be learnable from representative examples. In this paper, we introduce tractable learning algorithms for learning this class of grammars, comparing them in terms of a-priori ...
متن کاملMultiple Predicate Learning for Document Image Understanding
Document image understanding denotes the recognition of semantically relevant components in the layout extracted from a document image. This recognition process is based on some visual models, whose manual specification can be a highly demanding task. In order to automatically acquire these models, we propose the application of machine learning techniques. In this paper, problems raised by poss...
متن کاملDeep Learning For Smile Recognition
Inspired by recent successes of deep learning in computer vision, we propose a novel application of deep convolutional neural networks to facial expression recognition, in particular smile recognition. A smile recognition test accuracy of 99.45% is achieved for the Denver Intensity of Spontaneous Facial Action (DISFA) database, significantly outperforming existing approaches based on hand-craft...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: International Journal on Document Analysis and Recognition (IJDAR)
سال: 2021
ISSN: 1433-2833,1433-2825
DOI: 10.1007/s10032-021-00372-6