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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
SuperOCR: A Conversion from Optical Character Recognition to Image Captioning—0
On-Device Text Image Super Resolution—0
Clustering-based Automatic Construction of Legal Entity Knowledge Base from Contracts—0
On-Device Language Identification of Text in Images using Diacritic Characters—0
OCR Post Correction for Endangered Language TextsCode1
Automated data extraction of bar chart raster images—0
An Unsupervised method for OCR Post-Correction and Spelling Normalisation for FinnishCode1
Handwriting Classification for the Analysis of Art-Historical DocumentsCode0
Automated Transcription of Non-Latin Script Periodicals: A Case Study in the Ottoman Turkish Print Archive—0
OCR, Classification & Machine Translation (OCCAM)—0
Chunk-based Chinese Spelling Check with Global Optimization—0
Alleviating Digitization Errors in Named Entity Recognition for Historical DocumentsCode0
RUArt: A Novel Text-Centered Solution for Text-Based Visual Question AnsweringCode1
Persian Handwritten Digit, Character and Word Recognition Using Deep Learning—0
TLGAN: document Text Localization using Generative Adversarial NetsCode1
Boosting High-Level Vision with Joint Compression Artifacts Reduction and Super-Resolution—0
DE-GAN: A Conditional Generative Adversarial Network for Document EnhancementCode1
A Conglomerate of Multiple OCR Table Detection and Extraction—0
DocStruct: A Multimodal Method to Extract Hierarchy Structure in Document for General Form Understanding—0
Tokenization Repair in the Presence of Spelling ErrorsCode1
Table Structure Recognition using Top-Down and Bottom-Up CuesCode1
Finding the Evidence: Localization-aware Answer Prediction for Text Visual Question Answering—0
A Large Multi-Target Dataset of Common Bengali Handwritten GraphemesCode1
Hamming OCR: A Locality Sensitive Hashing Neural Network for Scene Text Recognition—0
Towards Image-based Automatic Meter Reading in Unconstrained Scenarios: A Robust and Efficient Approach—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