Handwritten Text Detection via EAST Segmentation and OCR
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Solution Overview
Problem
Current document processing techniques are inefficient and inaccurate in detecting and analyzing handwritten text due to the wide variety of writing styles and languages, leading to resource wastage and errors in converting physical documents to digital format.
Innovation Solution
A processing platform utilizing machine learning models and image processing techniques, such as filtering and content reconfiguration, to identify and segment handwritten text within images, and convert it into digital format, incorporating deep learning models like Efficient and Accurate Scene Text Detector (EAST) for text localization and convolutional neural networks for classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current document processing techniques are used to detect and analyze handwritten text, then the system can process documents, but the detection accuracy is low and resource consumption is high
Solution Approach 1:
The patent segments handwritten text detection into multiple stages: initial text region detection using EAST model, followed by word-level segmentation, then character recognition. This hierarchical segmentation allows the system to focus computational resources only on relevant regions, improving accuracy while reducing overall resource consumption compared to processing entire documents uniformly.
Solution Approach 2:
The patent introduces an intermediary text detection layer between image input and final OCR recognition. The EAST model acts as an intermediary that first identifies text regions, which then guides subsequent processing. This intermediary step filters out non-text regions, preventing wasted computational resources on irrelevant areas while improving detection precision.
2Productivity
If manual document processing is used, then resource consumption is low, but processing time is excessive and productivity is low
Solution Approach 1:
The patent performs preliminary text region detection and segmentation before the main OCR recognition process. By pre-identifying text locations and boundaries using the EAST model, the system prepares processed data structures that accelerate the subsequent recognition phase, overall increasing productivity while reducing total processing time compared to manual methods.
Solution Approach 2:
The patent replaces manual mechanical document processing with an automated system combining computer vision (EAST model) and OCR technology. This substitution eliminates the need for human operators to manually transcribe handwritten text, dramatically increasing productivity from hours per document to seconds per document while reducing time loss.
3Reliability
If standard OCR techniques are used on handwritten text, then the process is simple, but recognition accuracy is poor due to writing style variations
Solution Approach 1:
The patent applies local quality by performing specialized image processing and enhancement operations specifically on detected text regions rather than entire documents. Each text region receives tailored preprocessing based on its local characteristics, improving recognition accuracy for handwritten text while avoiding unnecessary processing complexity in non-text areas.
Solution Approach 2:
The patent performs preliminary image processing and enhancement operations on text regions before they are fed to the recognition model. This includes normalization, filtering, and feature enhancement that prepare the data in advance, improving reliability of recognition without requiring the recognition model itself to be overly complex.
Data Source
AI summary
In some implementations, a device may receive an image that depicts handwritten text. The device may determine that a section of the image includes the handwritten text. The device may analyze, using a first image processing technique, the section to identify subsections of the section that include individual words of the handwritten text. The device may reconfigure, using a second image processing technique, the subsections to create preprocessed word images associated with the individual words. The device may analyze, using a word recognition model, the preprocessed word images to generate digitized words that are associated with the preprocessed word images. The device may verify, based on a reference data structure, that the digitized words correspond to recognized words of the word recognition model. The device may generate, based on verifying the digitized words, digital text according to a sequence of the digitized words in the section.