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1. WO2020068945 - NAMED ENTITY RECOGNITION WITH CONVOLUTIONAL NETWORKS

Publication Number WO/2020/068945
Publication Date 02.04.2020
International Application No. PCT/US2019/052906
International Filing Date 25.09.2019
IPC
G06F 17/27 2006.01
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
FELECTRIC DIGITAL DATA PROCESSING
17Digital computing or data processing equipment or methods, specially adapted for specific functions
20Handling natural language data
27Automatic analysis, e.g. parsing, orthograph correction
G06F 17/21 2006.01
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
FELECTRIC DIGITAL DATA PROCESSING
17Digital computing or data processing equipment or methods, specially adapted for specific functions
20Handling natural language data
21Text processing
G06K 9/46 2006.01
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
9Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
36Image preprocessing, i.e. processing the image information without deciding about the identity of the image
46Extraction of features or characteristics of the image
CPC
G06K 2209/01
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
2209Indexing scheme relating to methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
01Character recognition
G06K 9/00449
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
9Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
00442Document analysis and understanding; Document recognition
00449Layout structured with printed lines or input boxes, e.g. business forms, tables
G06K 9/00463
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
9Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
00442Document analysis and understanding; Document recognition
00463Document analysis by extracting the geometrical structure, e.g. layout tree; Block segmentation, e.g. bounding boxes for graphics, paragraphs, words or letters
G06K 9/00469
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
9Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
00442Document analysis and understanding; Document recognition
00469Document understanding by extracting the logical structure, e.g. chapters, sections, columns, titles, paragraphs, captions, page number, and identifying its elements, e.g. author, keywords, ZIP code, money amount
G06K 9/325
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
9Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
20Image acquisition
32Aligning or centering of the image pick-up or image-field
3233Determination of region of interest
325Detection of text region in scene imagery, real life image or Web pages, e.g. licenses plates, captions on TV images
G06K 9/4628
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
9Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
36Image preprocessing, i.e. processing the image information without deciding about the identity of the image
46Extraction of features or characteristics of the image
4604Detecting partial patterns, e.g. edges or contours, or configurations, e.g. loops, corners, strokes, intersections
4609by matching or filtering
4619Biologically-inspired filters, e.g. receptive fields
4623with interaction between the responses of different filters
4628Integrating the filters into a hierarchical structure
Applicants
  • LEVERTON HOLDING LLC [US]/[US]
Inventors
  • SCHÄFER, Christian
Agents
  • REIBMAN, Andrew L.
  • MAJEWSKI, Dennis
  • HUBBARD, Nolan
  • REIBMAN, Andrew, L.
  • PLUMMER, Kelly, A.
  • NOVAK, Brian
Priority Data
62/736,92226.09.2018US
Publication Language English (EN)
Filing Language English (EN)
Designated States
Title
(EN) NAMED ENTITY RECOGNITION WITH CONVOLUTIONAL NETWORKS
(FR) RECONNAISSANCE D'ENTITÉ NOMMÉE AVEC DES RÉSEAUX CONVOLUTIFS
Abstract
(EN)
Methods and systems for recognizing named entities within the text of a document are provided. The methods and systems may include receiving a document image and recognized text of the document image. A feature map of the document image may be created, a tagged map may be created, and locations of tags within the tagged map may be estimated using a machine learning model. Named entities with the recognized text may be recognized based on the one or more locations of the tags. In some embodiments, the machine learning model is a convolutional neural network. In further embodiments, creating the feature map may include determining, for a subset of the cells of the feature map, one or more features of the recognized text contained in a corresponding portion of the document image.
(FR)
L'invention concerne des procédés et des systèmes de reconnaissance d'entités nommées à l'intérieur du texte d'un document. Les procédés et les systèmes peuvent comprendre la réception d'une image de document et d'un texte reconnu de l'image de document. Une carte des caractéristiques de l'image de document peut être créée, une carte balisée peut être créée et les emplacements des balises au sien de la carte balisée peuvent être estimés en utilisant un modèle d'apprentissage automatique. Des entités nommées ayant le texte reconnu peuvent être reconnues sur la base desdits emplacements des balises. Dans certains modes de réalisation, le modèle d'apprentissage automatique est un réseau neuronal convolutif. Dans d'autres modes de réalisation, la création de la carte des caractéristiques peut consister à déterminer, pour un sous-ensemble des cellules de la carte des caractéristiques, une ou plusieurs caractéristiques du texte reconnu contenu dans une portion correspondante de l'image de document.
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