SOTAVerified

Optical Character Recognition (OCR)

Optical Character Recognition or Optical Character Reader (OCR) is the electronic or mechanical conversion of images of typed, handwritten or printed text into machine-encoded text, whether from a scanned document, a photo of a document, a scene-photo (for example the text on signs and billboards in a landscape photo, license plates in cars...) or from subtitle text superimposed on an image (for example: from a television broadcast)

Papers

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Showing 676–700 of 1209 papers

TitleStatusHype
PP-OCRv2: Bag of Tricks for Ultra Lightweight OCR SystemCode2
A Novel Machine Learning Based Approach for Post-OCR Error Detection—0
OCR Processing of Swedish Historical Newspapers Using Deep Hybrid CNN–LSTM Networks—0
A Multimodal Framework for Video Ads Understanding—0
LayoutReader: Pre-training of Text and Layout for Reading Order Detection—0
EKTVQA: Generalized use of External Knowledge to empower Scene Text in Text-VQA—0
Localize, Group, and Select: Boosting Text-VQA by Scene Text Modeling—0
Real-time Bangla License Plate Recognition System for Low Resource Video-based Applications—0
VisBuddy -- A Smart Wearable Assistant for the Visually Challenged—0
MMOCR: A Comprehensive Toolbox for Text Detection, Recognition and Understanding—0
BROS: A Pre-trained Language Model Focusing on Text and Layout for Better Key Information Extraction from DocumentsCode1
Lights, Camera, Action! A Framework to Improve NLP Accuracy over OCR documentsCode1
The Corpora They Are a-Changing: a Case Study in Italian Newspapers—0
MinD at SemEval-2021 Task 6: Propaganda Detection using Transfer Learning and Multimodal Fusion—0
Robust Learning for Text Classification with Multi-source Noise Simulation and Hard Example MiningCode1
Scene Text recognition with Full Normalization—0
Memes in the Wild: Assessing the Generalizability of the Hateful Memes Challenge Dataset—0
Data Centric Domain Adaptation for Historical Text with OCR ErrorsCode0
Automatic Metadata Extraction Incorporating Visual Features from Scanned Electronic Theses and DissertationsCode0
SAR-Net: Shape Alignment and Recovery Network for Category-level 6D Object Pose and Size Estimation—0
A Simple and Practical Approach to Improve Misspellings in OCR Text—0
An End-to-End Khmer Optical Character Recognition using Sequence-to-Sequence with Attention—0
Tag, Copy or Predict: A Unified Weakly-Supervised Learning Framework for Visual Information Extraction using Sequences—0
Scene Text Telescope: Text-Focused Scene Image Super-ResolutionCode0
Improving OCR-Based Image Captioning by Incorporating Geometrical Relationship—0
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All datasetsBenchmarking Chinese Text Recognition: Datasets, Baselines, and an Empirical StudyVideoDB's OCR Benchmark Public CollectionFSNS - TestI2L-140KSUTim2latex-100k

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DTrOCRAccuracy (%)89.6—Unverified
2DTrOCR 105MAccuracy (%)89.6—Unverified
3MaskOCR-LAccuracy (%)82.6—Unverified
4TransOCRAccuracy (%)72.8—Unverified
5SRNAccuracy (%)65—Unverified
6MORANAccuracy (%)64.3—Unverified
7SEEDAccuracy (%)61.2—Unverified
#ModelMetricClaimedVerifiedStatus
1GPT-4oAverage Accuracy76.22—Unverified
2Gemini-1.5 ProAverage Accuracy76.13—Unverified
3Claude-3 SonnetAverage Accuracy67.71—Unverified
4RapidOCRAverage Accuracy56.98—Unverified
5EasyOCRAverage Accuracy49.3—Unverified
#ModelMetricClaimedVerifiedStatus
1STREETSequence error27.54—Unverified
2SEESequence error22—Unverified
3AttentionOCR_Inception-resnet-v2_LocationSequence error15.8—Unverified
#ModelMetricClaimedVerifiedStatus
1I2L-NOPOOLBLEU89.09—Unverified
2I2L-STRIPSBLEU89—Unverified
#ModelMetricClaimedVerifiedStatus
1TesseractCharacter Error Rate (CER)0.08—Unverified
2EasyOCRCharacter Error Rate (CER)0.07—Unverified
#ModelMetricClaimedVerifiedStatus
1I2L-STRIPSBLEU88.86—Unverified