Tootfinder

Trump Admin Issues Finding That Harvard Permitted Antisemitism in Violation of Civil Rights Law (The Harvard Crimson)
https://www.thecrimson.com/article/2025/6/30/harvard-hhs-ocr-antisemitism-finding/
http://www.memeorandum.com/250630/p79#a250630p79

Zero-shot OCR Accuracy of Low-Resourced Languages: A Comparative Analysis on Sinhala and Tamil
Nevidu Jayatilleke, Nisansa de Silva
https://arxiv.org/abs/2507.18264 https://

Zero-shot OCR Accuracy of Low-Resourced Languages: A Comparative Analysis on Sinhala and Tamil
Solving the problem of Optical Character Recognition (OCR) on printed text for Latin and its derivative scripts can now be considered settled due to the volumes of research done on English and other High-Resourced Languages (HRL). However, for Low-Resourced Languages (LRL) that use unique scripts, it remains an open problem. This study presents a comparative analysis of the zero-shot performance of six distinct OCR engines on two LRLs: Sinhala and Tamil. The selected engines include both commer…

Evaluating VisualRAG: Quantifying Cross-Modal Performance in Enterprise Document Understanding
Varun Mannam, Fang Wang, Xin Chen
https://arxiv.org/abs/2506.21604

Evaluating VisualRAG: Quantifying Cross-Modal Performance in Enterprise Document Understanding
Current evaluation frameworks for multimodal generative AI struggle to establish trustworthiness, hindering enterprise adoption where reliability is paramount. We introduce a systematic, quantitative benchmarking framework to measure the trustworthiness of progressively integrating cross-modal inputs such as text, images, captions, and OCR within VisualRAG systems for enterprise document intelligence. Our approach establishes quantitative relationships between technical metrics and user-centric…

Comparing OCR Pipelines for Folkloristic Text Digitization
Octavian M. Machidon, Alina L. Machidon
https://arxiv.org/abs/2507.19092 https://arxiv.org/pdf/2…

Comparing OCR Pipelines for Folkloristic Text Digitization
The digitization of historical folkloristic materials presents unique challenges due to diverse text layouts, varying print and handwriting styles, and linguistic variations. This study explores different optical character recognition (OCR) approaches for Slovene folkloristic and historical text digitization, integrating both traditional methods and large language models (LLMs) to improve text transcription accuracy while maintaining linguistic and structural integrity. We compare single-stage …

ChatGPT: "Smart glasses combining AI scene understanding, OCR, and sensory substitution are decisive market disruptors" https://chatgpt.com/share/68349bdd-b670-8004-8788-893ca3f98ae5
"They render visual implants:
- Less competitive i…

ChatGPT - Tafelberaad Reflectieanalyse
Shared via ChatGPT

Kennt wer gute OCR-Tools, die auch Altgriechisch können?

OCR Complaint: Smith College (Defending Education)
https://defendinged.org/complaints/ocr-complaint-smith-college/
http://www.memeorandum.com/250623/p111#a250623p111

Bookmarked: calfa-co/hye-tesseract: Open OCR model for Armenian #Armenisch_OCR_Tesseract

Intelligent Automation for FDI Facilitation: Optimizing Tariff Exemption Processes with OCR And Large Language Models
Muhammad Sukri Bin Ramli
https://arxiv.org/abs/2506.12093

Intelligent Automation for FDI Facilitation: Optimizing Tariff Exemption Processes with OCR And Large Language Models
Tariff exemptions are fundamental to attracting Foreign Direct Investment (FDI) into the manufacturing sector, though the associated administrative processes present areas for optimization for both investing entities and the national tax authority. This paper proposes a conceptual framework to empower tax administration by leveraging a synergistic integration of Optical Character Recognition (OCR) and Large Language Model (LLM) technologies. The proposed system is designed to first utilize OCR …

#MistralAI Document #AI: Advanced #OCR solution for complex document processing 📄
📺

Design and Implementation of an OCR-Powered Pipeline for Table Extraction from Invoices
Parshva Dhilankumar Patel
https://arxiv.org/abs/2507.07029 https://…

Design and Implementation of an OCR-Powered Pipeline for Table Extraction from Invoices
This paper presents the design and development of an OCR-powered pipeline for efficient table extraction from invoices. The system leverages Tesseract OCR for text recognition and custom post-processing logic to detect, align, and extract structured tabular data from scanned invoice documents. Our approach includes dynamic preprocessing, table boundary detection, and row-column mapping, optimized for noisy and non-standard invoice formats. The resulting pipeline significantly improves data extr…

{tesseract} allows you to read text from images https://docs.ropensci.org/tesseract/ it can also be combined with {magick} https://

Open Source OCR Engine
Bindings to Tesseract: a powerful optical character recognition (OCR) engine that supports over 100 languages. The engine is highly configurable in order to tune the detection algorithms and obtain the best possible results.

To Claude: Explain how smart glasses with AI-based scene description, OCR for reading print, and visual-to-auditory sensory substitution for "raw vision" will erode the market opportunities for all implantable visual prostheses (retinal implants and brain implants for restoring vision to the blind). https://

For my next modern culture and media studies work, I'd like to use "THE SIMPLE NEW SOCIO-ECONOMICGEOPOLITICALBIO-HISTORICAL APPROACH" (TSR 1987)
(The eighties were a very weird time, y'all.)
(I found this in WCA #26, ZODIAC ATTACK)

FormGym: Doing Paperwork with Agents
Matthew Toles, Rattandeep Singh, Isaac Song Zhou Yu
https://arxiv.org/abs/2506.14079 https://arx…

FormGym: Doing Paperwork with Agents
Completing paperwork is a challenging and time-consuming problem. Form filling is especially challenging in the pure-image domain without access to OCR, typeset PDF text, or a DOM. For computer agents, it requires multiple abilities, including multi-modal understanding, information retrieval, and tool-use. We present a novel form-filling benchmark consisting of 432 fields spread across 55 documents and 3 tasks, requiring knowledge of 236 features per user. We find that baseline VLAs achieve les…

02:31—Good Night #Fediverse! 😴💤
I just made myself a new banner for myself, using (mostly) open source software. The only exception was that I used a random website to add an outline to my Rudolf Rocker image, I was too lazy to get out of bed and use GIMP on my PC for that step. For everything else, I used this tool from GitHub: 

🔄 Significantly improves #RAG pipeline performance by creating context-rich, high-quality text from documents that enhances #AI application accuracy
💼 Addresses critical business challenges where traditional #OCR

Bei dem ganzen KI-Gedöns würde ich ja denken, die perfekten und freien, sparsamen Modelle für Autokorrektur und OCR-Erkennung sollte da sein. So als quasi Kernkompetenz von LLMs. Aber hören und lesen tu ich hauptsächlich in Richtung "Chat"-Nutzung.

At a certain age, the paperwork you have at home is enormous. Official letters, invoices, prescriptions... But, of course, there is a self-hosted application that helps you manage all that clutter.
At the moment, I already have Paperless installed on the server using Docker. I'll leave for the weekend its configuration, testing its ocr (wow!) and its app for Android.
#selfhosting

VisionThink: Smart and Efficient Vision Language Model via Reinforcement Learning
Senqiao Yang, Junyi Li, Xin Lai, Bei Yu, Hengshuang Zhao, Jiaya Jia
https://arxiv.org/abs/2507.13348

VisionThink: Smart and Efficient Vision Language Model via Reinforcement Learning
Recent advancements in vision-language models (VLMs) have improved performance by increasing the number of visual tokens, which are often significantly longer than text tokens. However, we observe that most real-world scenarios do not require such an extensive number of visual tokens. While the performance drops significantly in a small subset of OCR-related tasks, models still perform accurately in most other general VQA tasks with only 1/4 resolution. Therefore, we propose to dynamically proc…

"Smart glasses offering a combination of sensory substitution based 'raw' vision and AI-based scene description and OCR appears to be technically and economically the most feasible and sustainable way toward meeting expectations, needs and interests of many blind people." https://www.artificialvision…

Uncertainty-Aware Complex Scientific Table Data Extraction
Kehinde Ajayi, Yi He, Jian Wu
https://arxiv.org/abs/2507.02009 https://arx…

Uncertainty-Aware Complex Scientific Table Data Extraction
Table structure recognition (TSR) and optical character recognition (OCR) play crucial roles in extracting structured data from tables in scientific documents. However, existing extraction frameworks built on top of TSR and OCR methods often fail to quantify the uncertainties of extracted results. To obtain highly accurate data for scientific domains, all extracted data must be manually verified, which can be time-consuming and labor-intensive. We propose a framework that performs uncertainty-a…

Email as the Interface to Generative AI Models: Seamless Administrative Automation
Andres Navarro, Carlos de Quinto, Jos\'e Alberto Hern\'andez
https://arxiv.org/abs/2506.23850

Email as the Interface to Generative AI Models: Seamless Administrative Automation
This paper introduces a novel architectural framework that integrates Large Language Models (LLMs) with email interfaces to automate administrative tasks, specifically targeting accessibility barriers in enterprise environments. The system connects email communication channels with Optical Character Recognition (OCR) and intelligent automation, enabling non-technical administrative staff to delegate complex form-filling and document processing tasks using familiar email interfaces. By treating …

Real-Time AI-Driven Pipeline for Automated Medical Study Content Generation in Low-Resource Settings: A Kenyan Case Study
Emmanuel Korir, Eugene Wechuli
https://arxiv.org/abs/2507.05212

Real-Time AI-Driven Pipeline for Automated Medical Study Content Generation in Low-Resource Settings: A Kenyan Case Study
Juvenotes is a real-time AI-driven pipeline that automates the transformation of academic documents into structured exam-style question banks, optimized for low-resource medical education settings in Kenya. The system combines Azure Document Intelligence for OCR and Azure AI Foundry (OpenAI o3-mini) for question and answer generation in a microservices architecture, with a Vue/TypeScript frontend and AdonisJS backend. Mobile-first design, bandwidth-sensitive interfaces, institutional tagging, a…