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Tensmeyer et al., 2019 - Google Patents

Training full-page handwritten text recognition models without annotated line breaks

Tensmeyer et al., 2019

Document ID
4865511544644463288
Author
Tensmeyer C
Wigington C
Publication year
Publication venue
2019 International conference on document analysis and recognition (ICDAR)

External Links

Snippet

Training Handwritten Text Recognition (HTR) models typically requires large amounts of labeled data which often are line or page images with corresponding line-level ground truth (GT) transcriptions. Many digital collections have page-level transcriptions for each image …
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Classifications

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    • G06F17/20—Handling natural language data
    • G06F17/27—Automatic analysis, e.g. parsing
    • G06F17/2765—Recognition
    • G06F17/277—Lexical analysis, e.g. tokenisation, collocates
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
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    • G06F17/28—Processing or translating of natural language
    • G06F17/2809—Data driven translation
    • G06F17/2827—Example based machine translation; Alignment
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    • G06F17/21—Text processing
    • G06F17/22—Manipulating or registering by use of codes, e.g. in sequence of text characters
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    • G06F17/3061—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F17/30634—Querying
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    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G—PHYSICS
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    • G06F17/30781—Information retrieval; Database structures therefor; File system structures therefor of video data
    • G06F17/30784—Information retrieval; Database structures therefor; File system structures therefor of video data using features automatically derived from the video content, e.g. descriptors, fingerprints, signatures, genre
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