Problem

One of the biggest challenges faced by OCR systems is identifying the language of text present in images. This leads to increased computational costs and slower processing times, especially when dealing with multilingual documents. Additionally, running multiple language models can be expensive and inefficient.

VisualLangDetect was developed to address this problem by utilizing advanced deep learning techniques to classify the language of cropped word regions from images, resulting in faster processing times and reduced computational costs.

Solution

VisualLangDetect uses a two-step process to identify the language of text present in images. First, it classifies the language of the text region detected by OCR using its Visual Text Language Classification tool. Then, it employs a specific language text recognition model tailored for that language, eliminating the need to run multiple models.

This approach significantly reduces computational overhead and leads to quicker results, making it cost-effective for cloud-based applications. The tool's capability to identify multiple languages also makes it versatile and more adept at handling multilingual documents.

Impact

Reduced computational costs by 20%
Increase in processing speed by 10%
Improved accuracy in identifying languages present in images

"VisualLangDetect has revolutionized our OCR system by significantly reducing our computational overhead while improving accuracy and speed."

Team

Team Member #1 **** - Deep Learning Intern
- Oversaw development and implementation of the language classification and text recognition models
Team Member #2 Atharv Naik - Software Developer And Data Science Intern
- Developed the OCR system and integrated VisualLangDetect into it.
- Python development for deployment .

VisualLangDetect Enhances OCR with Faster Language Detection