Tesseract
PulseAugur coverage of Tesseract — every cluster mentioning Tesseract across labs, papers, and developer communities, ranked by signal.
8 day(s) with sentiment data
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PDFs can trick AI models with hidden text discrepancies
A security researcher has detailed how PDF files can mislead AI models by presenting different information to human readers and extraction libraries. By altering the ToUnicode map within a PDF's font data, a single digi…
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New tool converts PDFs and Word docs to structured Markdown for RAG
A new tool has been developed to convert PDF and Word documents into structured Markdown, table rows, and retrieval-augmented generation (RAG) chunks. This tool aims to improve knowledge extraction by preserving documen…
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Python script converts PDFs to structured Markdown for RAG pipelines
This article presents a Python script using the Apify platform to convert PDFs into structured Markdown chunks suitable for retrieval-augmented generation (RAG) pipelines. The script addresses common issues with PDF tex…
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Indra OCR model boasts 15x speedup over Tesseract
Indra is a new text-recognition model with one million parameters that significantly outperforms Tesseract. It processes scans 15 times faster than Tesseract when running on a CPU. This open-source model is designed for…
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OmniParseBench: Document Parser Benchmark Analysis and OCR Testing
OmniParseBench, an open benchmark for document parsers, has been implemented and analyzed. This benchmark evaluates parsers using 16,288 tests across various document types, including scanned forms, handwriting, and den…
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RAG assistant built for scanned Hindi/English university circulars
A developer detailed the process of building a Retrieval-Augmented Generation (RAG) assistant for scanned university circulars in Hindi and English. Key challenges included optimizing OCR quality by detecting script per…
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Cloudflare and OpenAI explore computer vision integration in .NET
Cloudflare and OpenAI have collaborated on a research pilot, sharing early results on October 15th. The pilot focused on integrating computer vision capabilities within .NET applications, exploring various techniques su…
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New research tackles OCR errors with character verification and severity-weighted metrics · 4 sources tracked
Two new research papers address challenges in optical character recognition (OCR). The first introduces a Character Verification Model (CVM) to correct visually similar characters like 'O'/'0' and 'S'/'5', improving acc…
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Social Sciences DataLab releases fast Indra OCR model
Social Sciences DataLab has released Indra OCR, a new 1-million-parameter OCR model designed for high throughput and speed on CPUs. It can process up to 49 pages per second, significantly outperforming Tesseract and Pad…
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Build Secure Document Pipeline with OpenCV, Presidio, and LangGraph
This tutorial guides developers in building a secure document processing pipeline called SecureKiosk AI. The system uses local tools like OpenCV and Tesseract for image processing and text extraction, followed by Micros…
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Tesseract OCR deemed production-ready for specific document types
Tesseract OCR is deemed accurate enough for production use, particularly when processing documents that align with its training data. This assessment highlights its effectiveness for specific document types.
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LLMs struggle with noisy documents, new benchmark reveals
A new research paper benchmarks several open-source large language models (LLMs) for key-value pair extraction from documents, specifically examining their performance under Optical Character Recognition (OCR) noise. Th…
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Developer builds RAG platform to prevent confident hallucinations
A developer has created RAG.NextUpgrad, a platform designed to prevent retrieval-augmented generation (RAG) systems from confidently hallucinating answers. The platform prioritizes running on low-resource, free-tier hos…
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MLLMs struggle with low-resource Khmer documents, study finds
A new pilot study has evaluated the capabilities of multimodal large language models (MLLMs) in understanding low-resource Khmer documents. Researchers found that while current MLLMs can process visually clear English a…
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New dataset and pipeline advance Arabic manuscript OCR research
Researchers have introduced AraMS-28k, a large-scale dataset for historical Arabic manuscript recognition, featuring 28,600 annotated text lines across 14 books. This dataset includes detailed annotations for main text …
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Pipeline extracts billions of tokens from historical newspapers
Researchers have developed the Institutional Newspapers Pipeline, a modular system designed to extract high-quality, structured data from historical newspaper scans. This pipeline, created in collaboration with the Bost…
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Tesseract: A robust open-source OCR engine with broad language support
Tesseract is a long-standing open-source Optical Character Recognition (OCR) engine that supports over 100 languages. It is considered a baseline tool for OCR tasks and remains a notable open-source project in the devel…
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IBM's Docling offers self-hosted PDF-to-Markdown conversion for LLM pipelines
Docling, an open-source document parser developed by IBM, can convert various file types including PDFs, DOCX, and images into clean Markdown or JSON. This tool is particularly beneficial for LLM pipelines as it preserv…
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Production RAG pipelines require advanced architecture beyond simple demos
This article details the complexities of building a production-ready Retrieval-Augmented Generation (RAG) pipeline, contrasting it with simplified demo versions. It highlights common failure points such as outdated info…
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Google Research enables quantum computers to learn from errors
Google Research has developed a reinforcement learning framework that enables quantum computers to learn from their errors and self-correct during computations. This approach uses an autonomous agent to continuously adj…