Text recognition is a long-standing research problem for document digitalization. Existing
approaches are usually built based on CNN for image understanding and RNN for char …

Typical text recognition methods rely on an encoder-decoder structure, in which the encoder
extracts features from an image, and the decoder produces recognized text from these …

Unconstrained handwritten text recognition is a challenging computer vision task. It is
traditionally handled by a two-step approach, combining line segmentation followed by text …

Abstract Offline Handwritten Text Recognition (HTR) systems concern the automatic
recognition and transcription of handwritten text from scanned images to digital media …

Encoder-decoder models have made great progress on handwritten mathematical
expression recognition recently. However, it is still a challenge for existing methods to …

Historical document processing (HDP) corresponds to the task of converting the physical-
bind form of historical archives into a web-based centrally digitized form for their …

Handwritten Text Recognition has achieved an impressive performance in public
benchmarks. However, due to the high inter-and intra-class variability between handwriting …

The extraction of relevant information carried out by named entities in handwriting
documents is still a challenging task. Unlike traditional information extraction approaches …

Tracking droplets in microfluidics is a challenging task. The difficulty arises in choosing a
tool to analyze general microfluidic videos to infer physical quantities. The state-of-the-art …

Most recently, Transformers–which are recurrent-free neural network architectures–
achieved tremendous performances on various Natural Language Processing (NLP) tasks …