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 776–800 of 1209 papers

TitleStatusHype
What Machines See Is Not What They Get: Fooling Scene Text Recognition Models With Adversarial Text Images—0
What Media Frames Reveal About Stance: A Dataset and Study about Memes in Climate Change Discourse—0
Words as Geometric Features: Estimating Homography using Optical Character Recognition as Compressed Image Representation—0
Word Searching in Scene Image and Video Frame in Multi-Script Scenario using Dynamic Shape Coding—0
Word Segmentation from Unconstrained Handwritten Bangla Document Images using Distance Transform—0
You’ve translated it, now what?—0
An Ensemble of Neural Networks for Non-Linear Segmentation of Overlapped Cursive Script—0
Zero-Shot Learning Based Approach For Medieval Word Recognition Using Deep-Learned Features—0
1 Million Captioned Dutch Newspaper Images—0
Towards Self-Improvement of Diffusion Models via Group Preference Optimization—0
3D Rendering Framework for Data Augmentation in Optical Character Recognition—0
A Black-Box Attack on Optical Character Recognition Systems—0
A BLSTM Network for Printed Bengali OCR System with High Accuracy—0
Abstractive Information Extraction from Scanned Invoices (AIESI) using End-to-end Sequential Approach—0
A Classical Approach to Handcrafted Feature Extraction Techniques for Bangla Handwritten Digit Recognition—0
A comparative analysis of SRGAN models—0
A Comparative Study of Filtering Approaches Applied to Color Archival Document Images—0
A Compositional Textual Model for Recognition of Imperfect Word Images—0
A Conglomerate of Multiple OCR Table Detection and Extraction—0
A Cost Efficient Approach to Correct OCR Errors in Large Document Collections—0
Adapting Multilingual Embedding Models to Historical Luxembourgish—0
A Diachronic Corpus for Romanian (RoDia)—0
Ad Lingua: Text Classification Improves Symbolism Prediction in Image Advertisements—0
A document processing pipeline for the construction of a dataset for topic modeling based on the judgments of the Italian Supreme Court—0
Advanced ingestion process powered by LLM parsing for RAG system—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