Siamese Network Handwriting Variation Generation
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Solution Overview
Problem
Current Intelligent Character Recognition (ICR) engines are not capable of reliably converting images of handwritten text into textual content with low error rates, especially when dealing with variations in handwriting styles, leading to insufficient performance in real-life applications.
Innovation Solution
A system utilizing a Deeply Supervised Siamese Network (DSSN) and a text recognition framework that generates variations of known shreds to learn similarities in handwriting styles, allowing for accurate text recognition without the need for character segmentation, and reduces human labeling requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional ICR engines are used to convert handwritten text images into textual content, then the conversion process can be performed, but the error rate is high and reliability is insufficient
Solution Approach 1:
The system performs preliminary actions by generating multiple variations of each character before recognition. It creates synthetic training data with different handwriting styles, sizes, and orientations in advance, which prepares the model to handle diverse real-world handwriting variations during actual recognition, thereby improving both reliability and accuracy
Solution Approach 2:
The system changes parameters by transforming character images through various transformations including scaling, rotating, skewing, and distorting. These parameter changes create a diverse set of training examples that enable the recognition system to maintain high accuracy across different handwriting styles and conditions
2Measurement precision
If more handwritten text variations are used for training to improve recognition accuracy, then text recognition precision improves, but the amount of data required and processing complexity increases
Solution Approach 1:
The system creates copies of existing character images and applies transformations to generate synthetic training data. Instead of collecting vast amounts of real handwritten data, it copies available samples and variations them through digital transformations, achieving high accuracy without the complexity of large-scale data collection and manual labeling
Solution Approach 2:
The system replaces the mechanical process of manual data collection and labeling with automated digital image processing and synthesis. By using algorithmic transformations to generate training variations, it eliminates the need for extensive human annotation while maintaining high recognition accuracy
Data Source
AI summary
Introduced here is a machine learning related technique for supplying an observed model additional training data based upon previously received training data. To determine textual content of a character string based on a digital image that includes a handwritten version of the character string a substantial amount of training data is used. The character string can include one or more characters, and the characters can include any of letters, numerals, punctuation marks, symbols, spaces, etc. Disclosed herein is a technique to determine variations between different images of matching known character strings and substitute those variations into the images in order to create more images with the same known character string.