Overview
LSTM-based OCR system transcribing handwritten notes with 85% accuracy across diverse handwriting styles.
The Problem
Digitizing handwritten notes and forms is labor-intensive and error-prone when done manually.
The Solution
Implemented CRNN (CNN + LSTM) architecture with CTC loss for sequence-to-sequence transcription. Trained on IAM Handwriting Database with extensive augmentation.
Results & Impact
85%
Character Accuracy
On diverse styles
78%
Word Accuracy
Complete word recognition
0.3s
Processing Speed
Per line of text
Key Impact
- Automated form processing pipeline
- Reduced manual data entry by 70%
- Enabled searchable digital archives
Technologies Used
PythonTensorFlowLSTMCTC LossOpenCV