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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 551–575 of 1209 papers

TitleStatusHype
Abstractive Information Extraction from Scanned Invoices (AIESI) using End-to-end Sequential Approach—0
Is it possible to recover personal health information from an automatically de-identified corpus of French EHRs?—0
Corporate IT-support Help-Desk Process Hybrid-Automation Solution with Machine Learning Approach—0
Attacking Optical Character Recognition (OCR) Systems with Adversarial Watermarks—0
Towards Self-Improvement of Diffusion Models via Group Preference Optimization—0
CorA: A web-based annotation tool for historical and other non-standard language data—0
Advanced ingestion process powered by LLM parsing for RAG system—0
Is Cognition consistent with Perception? Assessing and Mitigating Multimodal Knowledge Conflicts in Document Understanding—0
Iterative Learning for Reliable Crowdsourcing Systems—0
Key Information Extraction in Purchase Documents using Deep Learning and Rule-based Corrections—0
Language Independent Single Document Image Super-Resolution using CNN for improved recognition—0
Convolutional Neural Networks for Font Classification—0
Convolutional Neural Networks for Automatic Meter Reading—0
Introducing One Sided Margin Loss for Solving Classification Problems in Deep Networks—0
ConvMath: A Convolutional Sequence Network for Mathematical Expression Recognition—0
Improving Long Handwritten Text Line Recognition with Convolutional Multi-way Associative Memory—0
An accurate and revised version of optical character recognition-based speech synthesis using LabVIEW—0
Improving Inference Performance of Machine Learning with the Divide-and-Conquer Principle—0
Improving Handwritten OCR with Training Samples Generated by Glyph Conditional Denoising Diffusion Probabilistic Model—0
Contrastive Graph Multimodal Model for Text Classification in Videos—0
1 Million Captioned Dutch Newspaper Images—0
A document processing pipeline for the construction of a dataset for topic modeling based on the judgments of the Italian Supreme Court—0
Improving OCR-Based Image Captioning by Incorporating Geometrical Relationship—0
Improving OCR Quality in 19th Century Historical Documents Using a Combined Machine Learning Based Approach—0
Introducing the Reference Corpus of Contemporary Portuguese Online—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