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 1151–1175 of 1209 papers

TitleStatusHype
Indigenous language technologies in Canada: Assessment, challenges, and successes—0
Indonesian ID Card Extractor Using Optical Character Recognition and Natural Language Post-Processing—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
Integrating Optical Character Recognition and Machine Translation of Historical Documents—0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
Intelligent Document Processing -- Methods and Tools in the real world—0
Interpretable Distance Metric Learning for Handwritten Chinese Character Recognition—0
Introducing One Sided Margin Loss for Solving Classification Problems in Deep Networks—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
Is Cognition consistent with Perception? Assessing and Mitigating Multimodal Knowledge Conflicts in Document Understanding—0
Is it possible to recover personal health information from an automatically de-identified corpus of French EHRs?—0
Iterative Learning for Reliable Crowdsourcing Systems—0
JaPOC: Japanese Post-OCR Correction Benchmark using Vouchers—0
JoyType: A Robust Design for Multilingual Visual Text Creation—0
K-Algorithm A Modified Technique for Noise Removal in Handwritten Documents—0
Key Information Extraction in Purchase Documents using Deep Learning and Rule-based Corrections—0
Khattat: Enhancing Readability and Concept Representation of Semantic Typography—0
KITAB-Bench: A Comprehensive Multi-Domain Benchmark for Arabic OCR and Document Understanding—0
Knowledge Discovery in Optical Music Recognition: Enhancing Information Retrieval with Instance Segmentation—0
Language Classification and Segmentation of Noisy Documents in Hebrew Scripts—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