CNN-Based Machine Translation System for Handwritten Text
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Solution Overview
Problem
Conventional OCR algorithms are inefficient for processing large text documents, struggle with accurate recognition of punctuations and digits, and are limited to specific fonts, failing to support translation of handwritten text across languages.
Innovation Solution
A method and system using a Convolution Neural Network (CNN) for machine translation, which processes images to generate patterns, maps them to words and attributes, initiates OCR conversion when thresholds are below a predefined level, and performs incremental learning for improved performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional OCR algorithms are used for character recognition, then character level processing is achieved, but processing time increases significantly and translation efficiency decreases
Solution Approach 1:
The patent segments the translation task into two paths: a fast path using CNN for common word patterns and a fallback path using OCR for unrecognized patterns. This segmentation allows most documents to be processed quickly while maintaining accuracy for complex cases.
Solution Approach 2:
The system performs preliminary action by pre-training the CNN on large datasets of common word patterns and punctuation. This pre-learning enables the system to quickly recognize frequent patterns without requiring full OCR processing, thereby improving speed while maintaining accuracy.
2Adaptability or versatility
If conventional OCR algorithms are used, then character level translation is possible, but the system fails to accurately identify punctuations and digits
Solution Approach 1:
The patent adds a new dimension to pattern recognition by using CNN to learn visual patterns of punctuation and digits directly from images, rather than relying on OCR's character-level analysis. This dimensional shift enables more accurate recognition of these challenging elements.
3Ease of manufacture
If conventional OCR mechanisms are implemented, then specific font text can be processed, but the system lacks robustness and cannot support multiple fonts
Solution Approach 1:
The CNN-based translation module serves as a universal processor that can handle multiple fonts, styles, and languages simultaneously. By learning visual patterns rather than relying on font-specific OCR rules, the system achieves multi-functionality and robustness across diverse text formats.
4Measurement precision
If conventional methods process text at character level, then detailed recognition is achieved, but significant time is required and handwritten translation is not supported
Solution Approach 1:
The patent merges character-level OCR with word-level CNN translation into a unified system. The CNN processes complete words and patterns in parallel, maintaining detailed recognition while dramatically reducing processing time compared to sequential character-level analysis.
Data Source
AI summary
A method and system for machine translation of text is disclosed. The method includes processing an image comprising a text to generate a pattern associated with the text based on a trained Convolution Neural Network (CNN). The method further includes mapping the pattern to a word in a mapping table and at least one text attribute, based on a classifier network. The method further includes initiating an Optical Character Recognition (OCR) conversion for the pattern, when at least one of the mapping between at least one of the pattern and at least one word in the mapping table and the mapping between the pattern and the at least one text attribute is below a predefined threshold. The method further includes performing incremental learning for the trained CNN and the classifier network based on the OCR conversion.