Assignment 2: Optical Character Recognition (OCR)

Objective
The objective of this assignment is to implement and compare two Optical Character Recognition (OCR) pipelines on a common evaluation task. The assignment involves working with both a traditional OCR system and a neural network–based OCR system using transfer learning, followed by a comparative analysis of their performance.

Part A: OCR using PyTesseract

In this part, an OCR pipeline is implemented using PyTesseract. PyTesseract is a ready-to-use OCR engine and does not require any training. The focus of this part is on preprocessing, configuration, and evaluation.

Tasks performed

• Load the provided test image(s) and perform OCR using PyTesseract
• Apply suitable image preprocessing techniques such as resizing, thresholding, noise removal, and morphological operations
• Experiment with different Page Segmentation Modes (PSM) and document the chosen configuration
• Report the recognized text output
• Compute and report evaluation metrics such as Character Error Rate (CER) and/or Word Error Rate (WER) on the provided evaluation page

Part B: OCR using Transfer Learning (EasyOCR)

In this part, a neural network–based OCR system is implemented using transfer learning. EasyOCR is used as the recognition framework.

Tasks performed

• Use the provided training dataset to fine-tune an EasyOCR recognition model
• Clearly document the transfer learning strategy used, including freezing layers, partial fine-tuning, and learning rate selection
• Train the model and report training metrics such as loss curves
• Run inference on the same evaluation image(s) used in Part A
• Compute and report CER and/or WER for the EasyOCR model

Documentation details
The implementation clearly documents:

• Which components of the model were fine-tuned
• What changes were made compared to the default pre-trained setup
• The reasoning behind these changes

Common Evaluation and Comparison

To ensure a fair comparison, both OCR systems are evaluated on the same provided test page.

Tasks performed

• Present the OCR outputs from both PyTesseract and EasyOCR
• Compare the quantitative metrics (CER and/or WER)
• Provide a brief qualitative discussion explaining which method performs better and why

Deliverables

• One Colab notebook for Part A (PyTesseract)
• One Colab notebook for Part B (EasyOCR with transfer learning)
• A short written report (1–2 pages) summarizing the results and comparison