Diagnostic SummaryMETRIC.SYS
ENGINEERChenchukrishna Akkarapalli
TIMELINEDec - 2025
CATEGORYHR - OCR
STACK_BASEPython
OCR Handwritten Text Extraction — Bank Deposit Forms
01 // Technical Overview
Designed and implemented a high-accuracy handwritten OCR system for bank deposit forms by integrating computer vision, object detection, and transformer-based OCR models.
02 // Core Architecture
Built an end-to-end OCR pipeline using YOLOv8, OpenCV, Roboflow, and Hugging Face models to extract handwritten and printed fields from structured bank deposit forms.
Used YOLOv8 for precise field-level detection (account number, amount, date, depositor name, PAN, etc.), ensuring region-specific OCR instead of full-image extraction.
Prepared and managed annotated datasets using Roboflow, enabling efficient labeling, augmentation, and version control for OCR training.
Applied OpenCV preprocessing techniques (grayscale conversion, noise removal, thresholding, perspective correction) to improve handwriting clarity and OCR confidence.
Integrated Hugging Face Transformer-based OCR models (handwritten text recognition) to extract text with high accuracy and consistency.
Implemented confidence filtering and validation logic to reject low-confidence predictions and improve overall reliability.
Structured extracted data into JSON / database-ready formats for downstream processing and validation.
Optimized the pipeline for real-world banking scenarios, handling variations in handwriting styles, lighting conditions, and form alignment.
03 // Flow & Methodology
1Image Input → Bank deposit form scan/photo
2Preprocessing → OpenCV (denoise, threshold, alignment)
3Field Detection → YOLOv8 (trained via Roboflow annotations)
4Text Recognition → Hugging Face handwritten OCR model
5Post-processing → Confidence scoring, validation, formatting
6Output → Structured digital data for banking workflows
OCR Pipeline
04 // Tech Stack & Environment
Core Tooling TelemetrySYS.READY
›Languages: Python
›Object Detection: YOLOv8
›OCR Models: Hugging Face (Handwritten OCR)
›Dataset Management: Roboflow
›Image Processing: OpenCV
›Output Formats: JSON, CSV, Database-ready schemas
05 // Impact Analysis
✦Achieved high-confidence handwritten text extraction suitable for financial documents.
✦Reduced manual data entry and verification effort.
✦Built a scalable OCR framework adaptable to other structured forms.
REPORT GENERATED BYChenchukrishna Akkarapalli
