Optical Character Recognition (OCR) is the process of turning images that contain text—such as scanned pages, receipts, or photographs—into machine-readable text. What began as brittle rule-based systems has evolved into a rich ecosystem of neural architectures and vision-language models capable of reading complex, multi-lingual, and handwritten documents.
How OCR Works?
Every OCR system tackles three…
A team of researchers from Meta Reality Labs and Carnegie Mellon University has introduced MapAnything, an end-to-end transformer architecture that directly regresses factored metric 3D scene geometry from images and optional sensor inputs. Released under Apache 2.0 with full training and benchmarking code, MapAnything advances beyond specialist pipelines by supporting over 12 distinct 3D vision…
IBM has released Granite-Docling-258M, an open-source (Apache-2.0) vision-language model designed specifically for end-to-end document conversion. The model targets layout-faithful extraction—tables, code, equations, lists, captions, and reading order—emitting a structured, machine-readable representation rather than lossy Markdown. It is available on Hugging Face with a live demo and MLX build for Apple Silicon.
What’s new compared to…
In this tutorial, we explore advanced computer vision techniques using TorchVision’s v2 transforms, modern augmentation strategies, and powerful training enhancements. We walk through the process of building an augmentation pipeline, applying MixUp and CutMix, designing a modern CNN with attention, and implementing a robust training loop. By running everything seamlessly in Google Colab, we position…
Computer vision moved fast in 2025: new multimodal backbones, larger open datasets, and tighter model–systems integration. Practitioners need sources that publish rigorously, link code and benchmarks, and track deployment patterns—not marketing posts. This list prioritizes primary research hubs, lab blogs, and production-oriented engineering outlets with consistent update cadence. Use it to monitor SOTA shifts, grab…
How do you create 3D datasets to train AI for Robotics without expensive traditional approaches? A team of researchers from NVIDIA released “ViPE: Video Pose Engine for 3D Geometric Perception” bringing a key improvement for Spatial AI. It addresses the central, agonizing bottleneck that has constrained the field of 3D computer vision for years.
ViPE…
Introduction
Vision Language Models (VLMs) allow both text inputs and visual understanding. However, image resolution is crucial for VLM performance for processing text and chart-rich data. Increasing image resolution creates significant challenges. First, pretrained vision encoders often struggle with high-resolution images due to inefficient pretraining requirements. Running inference on high-resolution images increases computational costs and…
Introduction
Understanding how the brain builds internal representations of the visual world is one of the most fascinating challenges in neuroscience. Over the past decade, deep learning has reshaped computer vision, producing neural networks that not only perform at human-level accuracy on recognition tasks but also seem to process information in ways that resemble our…
Multimodal foundation models (MFMs) like GPT-4o, Gemini, and Claude have shown rapid progress recently, especially in public demos. While their language skills are well studied, their true ability to understand visual information remains unclear. Most benchmarks used today focus heavily on text-based tasks, such as VQA or classification, which often reflect language strengths more than…
Contrastive Language-Image Pre-training (CLIP) has become important for modern vision and multimodal models, enabling applications such as zero-shot image classification and serving as vision encoders in MLLMs. However, most CLIP variants, including Meta CLIP, are limited to English-only data curation, ignoring a significant amount of non-English content from the worldwide web. Scaling CLIP to include…