Language-Agnostic OCR Word Embeddings for Multilingual Extraction
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Solution Overview
Problem
Existing OCR technologies face challenges in efficiently extracting text from digital images in multilingual and online environments, leading to errors and high computational demands due to character-by-character processing and language-specific model requirements.
Innovation Solution
A multimodal language agnostic machine learning model that encodes semantically similar text and images in the same latent space, enabling word-level recognition without language detection, using a Turing Bletchley model trained on unlabeled text translation pairs and a dictionary search for text extraction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If character-by-character OCR processing is used, then text extraction can be performed, but computational time and resource requirements increase significantly
Solution Approach 1:
The patent segments the OCR process into two distinct stages: (1) word-level recognition using a language-agnostic model to identify bounding boxes of words in the image, and (2) text extraction using a language-specific decoder for the identified words. This segmentation allows the computationally intensive word detection to be performed once at the word level rather than character-by-character, significantly reducing computational time while maintaining accuracy.
Solution Approach 2:
The patent introduces a language-agnostic intermediate representation (word-level bounding boxes and embeddings) that bridges the gap between image input and text output. This intermediary stage allows the system to avoid direct character-by-character processing by first identifying words as complete units, then extracting their text content separately, thereby reducing overall computational complexity.
2Measurement precision
If language-specific OCR models are used for each language, then text recognition accuracy improves, but device complexity and resource requirements increase
Solution Approach 1:
The patent employs a universal language-agnostic word recognition model that can process text in multiple languages without requiring separate models for each language. This single model performs word-level detection and embedding generation for any language, and then a language-specific decoder is applied only to the identified words. This universal approach eliminates the need to maintain multiple complete OCR model chains for different languages, reducing device complexity while preserving accuracy through the targeted use of language-specific decoders.
3Productivity
If traditional OCR systems are deployed in online environments, then text extraction can be performed, but computational resources and processing speed are insufficient for large-scale content distribution
Solution Approach 1:
By segmenting the OCR pipeline into language-agnostic word detection and language-specific text extraction, the system performs the computationally heavy word identification task once per image rather than processing each character individually. This segmentation enables efficient batch processing of millions of images in online environments, dramatically improving throughput while reducing per-image computational resource consumption.
Solution Approach 2:
The patent changes the fundamental parameter of processing granularity from character-level to word-level. This parameter change reduces the number of processing operations required per image by approximately 5-10 times (since words contain multiple characters), enabling the system to handle large-scale content distribution in online environments with acceptable computational resource usage.
Data Source
AI summary
Technologies for language agnostic OCR extraction include identifying a word region of an image using optical character recognition, applying a language agnostic machine learning model to the word region, where the language agnostic machine learning model is trained on training data including a set of image-text pairs and a set of multilingual text translation pairs, receiving, from the language agnostic machine learning model, a word region embedding that is associated with the word region, searching a multilingual index for a text embedding that matches the word region embedding, receiving, from the multilingual index, text associated with the text embedding; and outputting at least one of the text or the text embedding to at least one downstream process, application, system, component, or network.