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 576–600 of 1209 papers

TitleStatusHype
An accurate and revised version of optical character recognition-based speech synthesis using LabVIEW—0
Improving Text Generation on Images with Synthetic Captions—0
IncepText: A New Inception-Text Module with Deformable PSROI Pooling for Multi-Oriented Scene Text Detection—0
Improving Inference Performance of Machine Learning with the Divide-and-Conquer Principle—0
Indigenous language technologies in Canada: Assessment, challenges, and successes—0
Corporate IT-support Help-Desk Process Hybrid-Automation Solution with Machine Learning Approach—0
Indonesian ID Card Extractor Using Optical Character Recognition and Natural Language Post-Processing—0
Improving Handwritten OCR with Training Samples Generated by Glyph Conditional Denoising Diffusion Probabilistic Model—0
Information Extraction from Scanned Invoice Images using Text Analysis and Layout Features—0
Information Extraction from Unstructured data using Augmented-AI and Computer Vision—0
Information Retrieval from the Digitized Books—0
Correction of OCR Word Segmentation Errors in Articles from the ACL Collection through Neural Machine Translation Methods—0
Integrating Optical Character Recognition and Machine Translation of Historical Documents—0
Integration of Text-maps in Convolutional Neural Networks for Region Detection among Different Textual Categories—0
Intelligent Automation for FDI Facilitation: Optimizing Tariff Exemption Processes with OCR And Large Language Models—0
Contrastive Graph Multimodal Model for Text Classification in Videos—0
1 Million Captioned Dutch Newspaper Images—0
Large Language Models for Page Stream Segmentation—0
Improving Document Clustering by Removing Unnatural Language—0
Improving Amharic Handwritten Word Recognition Using Auxiliary Task—0
Introducing the Reference Corpus of Contemporary Portuguese Online—0
Investigating the Decoders of Maximum Likelihood Sequence Models: A Look-ahead Approach—0
Invisible Threats: Backdoor Attack in OCR Systems—0
Context-Independent OCR with Multimodal LLMs: Effects of Image Resolution and Visual Complexity—0
Improve Sentence Alignment by Divide-and-conquer—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