Vast collections of documents available in image format need to be indexed for information
retrieval purposes. In this framework, word spotting is an alternative solution to optical …

The problem of answering questions about an image is popularly known as visual question
answering (or VQA in short). It is a well-established problem in computer vision. However …

In this work we present an end-to-end system for text spotting—localising and recognising
text in natural scene images—and text based image retrieval. This system is based on a …

This paper addresses the problems of word spotting and word recognition on images. In
word spotting, the goal is to find all instances of a query word in a dataset of images. In …

In recent years, deep convolutional neural networks have achieved state of the art
performance in various computer vision tasks such as classification, detection or …

Handwriting has remained one of the most frequently occurring patterns that we come
across in everyday life. Handwriting offers a number of interesting pattern classification …

In this paper we present an efficient segmentation-free word spotting method, applied in the
context of historical document collections, that follows the query-by-example paradigm. We …

This paper presents a systematic literature review of image datasets for document image
analysis, focusing on historical documents, such as handwritten manuscripts and early …

Nowadays, deep learning methods are employed in a broad range of research fields. The
analysis and recognition of historical documents, as we survey in this work, is not an …

Recurrent neural networks (RNN) have been successfully applied for recognition of cursive
handwritten documents, both in English and Arabic scripts. Ability of RNNs to model context …