PATENT CLAIM ANALYSIS

Application Number: 15887653
Application Type: Utility
Filing Date: 2018-02
Publication Date: 2018-06
Patent Classification: ["382", "173000"]

Abstract:
A method, an apparatus and an electronic device of character segmentation are disclosed. The method includes obtaining character segmentation points of a character image to be segmented as candidate segmentation points using a predetermined segmentation point generation algorithm, the character image to be segmented being a foreground character image that is obtained by removing a background image from an original grayscale character image; selecting and obtaining correct segmentation points from the candidate segmentation points based on the original grayscale character image and a pre-generated segmentation point classifier; and performing character segmentation for the character image to be segmented based on the correct segmentation points. Using the method provided by the present disclosure, candidate segmentation points can be filtered to obtain correct segmentation points, thus avoiding overly segmentation of a character image having phenomena such as character breaking, and thereby achieving an effect of improvement on the accuracy of character segmentation.

Claim (Index 4):
The method of  claim 3 , wherein the machine learning algorithm uses a convolutional neural network algorithm, and obtaining the pre-generated segmentation point classifier by learning through the machine learning algorithm comprises:\n traversing training data in the training set, taking historical foreground character images included in the training data as input images of a predetermined convolutional neural network model, and computing and obtaining outputs of the predetermined convolutional neural network model as prediction results of the training data; calculating a squared sum of differences between the prediction results of the training data and the actual results as an error of a current iteration of the training set; determining whether the error of the current iteration is less than an error of a previous iteration; if affirmative, adjusting a weight matrix of the predetermined convolutional neural network model based on a predetermined training rate and returning to the operation of traversing training data in the training set to continue the training; and if not, setting an adjusted convolutional neural network model as the segmentation point classifier.

Metadata:
- Claim Count in Document: 9.0
- Percentile: 88.0
- Lexical Diversity: 2.28571
- Patent Class: 382.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: True
- Related Applications: ['15549126', '11356449', '14174424', '14443918', '13849588']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3790057598600158
- 35 USC 102 Novelty (BERT): 0.51759222984572
- Combined Prediction Score: 0.3928644068585862
- Mean Citation Score: 256.34235
- Max Citation Score: 275.8499
- Similarity Product: 159.82728489810228

Labels:
- Claim Label 101: 1
- Claim Label 102: 1
- Claim Label 103: 1
- Claim Label 112: 1
- Combined Label: 1
- Label 101 Adjusted: 1

Dataset: test