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 976–1000 of 1209 papers

TitleStatusHype
FINN-L: Library Extensions and Design Trade-off Analysis for Variable Precision LSTM Networks on FPGAsCode0
Sum-Product Networks for Sequence Labeling—0
Calamari - A High-Performance Tensorflow-based Deep Learning Package for Optical Character RecognitionCode0
Multi-Input Attention for Unsupervised OCR Correction—0
ASTER: An Attentional Scene Text Recognizer with Flexible RectificationCode0
Deductron -- A Recurrent Neural Network—0
Recommending Scientific Videos based on Metadata Enrichment using Linked Open Data—0
NRTR: A No-Recurrence Sequence-to-Sequence Model For Scene Text RecognitionCode0
Confidence Prediction for Lexicon-Free OCR—0
Implicit Language Model in LSTM for OCRCode0
IncepText: A New Inception-Text Module with Deformable PSROI Pooling for Multi-Oriented Scene Text Detection—0
PDFdigest: an Adaptable Layout-Aware PDF-to-XML Textual Content Extractor for Scientific Articles—0
PDF-to-Text Reanalysis for Linguistic Data Mining—0
Building a Corpus from Handwritten Picture Postcards: Transcription, Annotation and Part-of-Speech Tagging—0
TF-LM: TensorFlow-based Language Modeling ToolkitCode0
Towards Processing of the Oral History Interviews and Related Printed Documents—0
Building A Handwritten Cuneiform Character Imageset—0
PDFAnno: a Web-based Linguistic Annotation Tool for PDF DocumentsCode0
Delta vs. N-Gram Tracing: Evaluating the Robustness of Authorship Attribution Methods—0
D\'etection d'erreurs dans des transcriptions OCR de documents historiques par r\'eseaux de neurones r\'ecurrents multi-niveau (Combining character level and word level RNNs for post-OCR error detection)—0
Computer-assisted Speaker Diarization: How to Evaluate Human Corrections—0
Measuring Innovation in Speech and Language Processing Publications.—0
Matics Software Suite: New Tools for Evaluation and Data Exploration—0
Low-resource Post Processing of Noisy OCR Output for Historical Corpus Digitisation—0
Correction of OCR Word Segmentation Errors in Articles from the ACL Collection through Neural Machine Translation Methods—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