Optical character recognition (OCR) is one of the most popular techniques used for
converting printed documents into machine-readable ones. While OCR engines can do well …

The field of machine translation (MT), the automatic translation of written text from one
natural language into another, has experienced a major paradigm shift in recent years …

Visual modifications to text are often used to obfuscate offensive comments in social media
(eg,"! d10t") or as a writing style (" 1337" in" leet speak"), among other scenarios. We …

We present a neural model for morphological inflection generation which employs a hard
attention mechanism, inspired by the nearly-monotonic alignment commonly found between …

Abstract Large Language Models (LLMs) have demonstrated remarkable performance in
various tasks and gained significant attention. LLMs are also used for local sequence …

There is little to no data available to build natural language processing models for most
endangered languages. However, textual data in these languages often exists in formats …

For indexing the content of digitized historical texts, optical character recognition (OCR)
errors are a hampering problem. To explore the effectivity of new strategies for OCR post …

We propose a novel approach to OCR post-correction that exploits repeated texts in large
corpora both as a source of noisy target outputs for unsupervised training and as a source of …

Optical character recognition (OCR) is crucial for a deeper access to historical collections.
OCR needs to account for orthographic variations, typefaces, or language evolution (ie, new …

Much of the existing linguistic data in many languages of the world is locked away in non-
digitized books and documents. Optical character recognition (OCR) can be used to produce …