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 551–575 of 1209 papers

TitleStatusHype
A Novel Transfer Learning Approach upon Hindi, Arabic, and Bangla Numerals using Convolutional Neural Networks—0
A Hybrid Swarm and Gravitation based feature selection algorithm for Handwritten Indic Script Classification problem—0
Efficient Medical VIE via Reinforcement Learning—0
Building OCR/NER Test Collections—0
Efficient Media Retrieval from Non-Cooperative Queries—0
A Novel Pipeline for Improving Optical Character Recognition through Post-processing Using Natural Language Processing—0
Efficient, Lexicon-Free OCR using Deep Learning—0
Efficient few-shot learning for pixel-precise handwritten document layout analysis—0
Building A Handwritten Cuneiform Character Imageset—0
Efficient End-to-End Visual Document Understanding with Rationale Distillation—0
Effective search space reduction for spell correction using character neural embeddings—0
Building a Corpus from Handwritten Picture Postcards: Transcription, Annotation and Part-of-Speech Tagging—0
A Novel Method for the Recognition of Isolated Handwritten Arabic Characters—0
A Hybrid Defense Method against Adversarial Attacks on Traffic Sign Classifiers in Autonomous Vehicles—0
Effectiveness of Mining Audio and Text Pairs from Public Data for Improving ASR Systems for Low-Resource Languages—0
Éclair -- Extracting Content and Layout with Integrated Reading Order for Documents—0
Budget-Optimal Task Allocation for Reliable Crowdsourcing Systems—0
EASTER: Efficient and Scalable Text Recognizer—0
BROS: A Pre-trained Language Model for Understanding Texts in Document—0
A Novel Machine Learning Based Approach for Post-OCR Error Detection—0
Broken News: Making Newspapers Accessible to Print-Impaired—0
A Novel Approach to Skew-Detection and Correction of English Alphabets for OCR—0
E2E Process Automation Leveraging Generative AI and IDP-Based Automation Agent: A Case Study on Corporate Expense Processing—0
Dynamic Programming Approach to Template-based OCR—0
Dynamic Low-Resolution Distillation for Cost-Efficient End-to-End Text Spotting—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