Handwriting text recognition (HTR) remains a challenging task. Existing approaches require fine-tuning on labeled data, which is impractical to obtain for real-world problems, or rely on zero-shot tools such as OCR engines and multi-modal LLMs (MLLMs). MLLMs have shown promise both as end-to-end transcribers and as OCR post-processors, but to date there is little empirical research evaluating different MLLM prompting strategies for HTR, particularly for the case of multi-page documents. Most handwritten documents are multi-page, and share context such as semantic content and handwriting style across pages, yet MLLMs are typically used for transcription at the page level, meaning they throw away this shared context. They are also typically used as either text-only post-processors or image-only OCR alternatives, rather than leveraging multiple modes. This paper investigates a suite of methods combining OCR, LLM post-processing and MLLM end-to-end transcription, for the task of zero-shot multi-page handwritten document transcription. We introduce a benchmark for this task from existing single-page datasets, including a new dataset, Malvern-Hills. Finally, we introduce OCR+PAGE-1 and OCR+PAGE-N, prompting strategies for multi-page transcription that outperform existing methods by sharing content across pages while minimizing prompt complexity.

A large-scale multi-species dataset of acoustic recordings Dataset compatible with two papers: The Computational Paralinguistics ChallengE (ComParE): Mosquito Event Detection Task https://github.com/EIHW/ComParE2022/tree/MOS-C An update to: HumBugDB: a large-scale acoustic mosquito dataset: NeurIPS 2021 Paper https://github.com/HumBug-Mosquito/HumBugDB. A large-scale multi-species dataset containing recordings of mosquitoes collected from multiple locations globally, as well as via different collection methods. In total, we present 20 hours of labelled mosquito data with 15 hours of corresponding background noise, recorded at the sites of 8 experiments. Of these, 64,843 seconds contain species metadata, consisting of 36 species (or species complexes). This repository contains: Audio files to be extracted into audio/data/train and audio/data/dev/{a/b} respectively Metadata in csv format: neurips_2021_zenodo_0_0_2.csv

Abstract Mosquito surveys are time‐consuming, expensive and can provide a biased spatial sample of occurrence data—the data often representing the location of the surveys, not the occurrence of the mosquitoes. We present the HumBug project, an acoustic system that can turn any Android smartphone into a mosquito sensor. Our sensor has the potential to significantly increase the quantity of mosquito occurrence data as well as access locations that are more difficult to survey by traditional means. We describe our database of wild‐captured mosquito fight tone audio data and outline our mosquito detection algorithms that these data train. We also present our MozzWear App, designed to work on budget smartphones, which, together with our HumBug Net (an adapted traditional bednet), facilitates data collection and allows the user to record and directly upload mosquito flight tones from any dwelling with a bednet in the field. Our HumBug system has the potential to vastly increase our understanding of the distribution of mosquito species in space and time and greatly improve surveys needed to assess the success or failure of ongoing vector control measures. At a time when the WHO reports a plateauing in the decade‐long decline in malaria mortality rates, this new technological solution for surveying mosquito vectors will provide a timely new resource.

This paper presents the first large-scale multi-species dataset of acoustic recordings of mosquitoes tracked continuously in free flight. We present 20 hours of audio recordings that we have expertly labelled and tagged precisely in time. Significantly, 18 hours of recordings contain annotations from 36 different species. Mosquitoes are well-known carriers of diseases such as malaria, dengue and yellow fever. Collecting this dataset is motivated by the need to assist applications which utilise mosquito acoustics to conduct surveys to help predict outbreaks and inform intervention policy. The task of detecting mosquitoes from the sound of their wingbeats is challenging due to the difficulty in collecting recordings from realistic scenarios. To address this, as part of the HumBug project, we conducted global experiments to record mosquitoes ranging from those bred in culture cages to mosquitoes captured in the wild. Consequently, the audio recordings vary in signal-to-noise ratio and contain a broad range of indoor and outdoor background environments from Tanzania, Thailand, Kenya, the USA and the UK. In this paper we describe in detail how we collected, labelled and curated the data. The data is provided from a PostgreSQL database, which contains important metadata such as the capture method, age, feeding status and gender of the mosquitoes. Additionally, we provide code to extract features and train Bayesian convolutional neural networks for two key tasks: the identification of mosquitoes from their corresponding background environments, and the classification of detected mosquitoes into species. Our extensive dataset is both challenging to machine learning researchers focusing on acoustic identification, and critical to entomologists, geo-spatial modellers and other domain experts to understand mosquito behaviour, model their distribution, and manage the threat they pose to humans.