A database can accept a schema that is logically wrong. Every CREATE TABLE statement may execute successfully while an intended one-to-one relationship behaves as one-to-many, an optional relationship becomes mandatory, or a product code loses leading zeros because it was modeled as an integer.
LoveOCR’s ERD conversion tool recognizes entity boxes, attributes, relationship lines, primary and foreign keys and common cardinality notation. After generation, validation should concentrate on meaning. The goal is not only “does the database accept this DDL?” but “does the database enforce the same rules the diagram was trying to communicate?”
Make a constraint map from the diagram
For each relationship, write a short sentence in ordinary language before inspecting SQL: “Each order belongs to exactly one customer; a customer can have zero or many orders.” That sentence contains minimum and maximum cardinality on both sides. It is easier to compare a SQL constraint to a sentence than to mentally decode a crowded line in a diagram.
Repeat this for key attributes: “Email is unique but not the primary key,” “An order item is identified by order plus line number,” or “External reference may be absent.” This small map becomes a test plan.
Primary keys: identity is not the same as uniqueness
A primary key must uniquely and stably identify a row. A field can be unique without being a good primary key. Email addresses, names and phone numbers can change. Composite business keys can be appropriate in some models, but a generated surrogate ID may be easier to reference. Use the diagram and domain, not a one-size-fits-all rule.
Check whether the generator accidentally created multiple candidate IDs or dropped a composite key marker. If the source shows a composite key, verify that uniqueness applies to the combination rather than each column separately.
Foreign keys: verify both type and direction
A foreign-key column should use a compatible type with the referenced key. More importantly, it should live on the side that matches the relationship. For one customer to many orders, the common implementation is a customer key in Orders. Putting an order key on Customer would limit or distort the relationship.
Names can mislead. A line labeled “owner” may point visually in one direction while the database reference belongs on the opposite table. Use cardinality and record examples to decide.
One-to-one needs uniqueness somewhere
Adding a foreign key alone generally allows many child rows to reference the same parent. If the model is truly one-to-one, the referencing column often needs a unique constraint, or the two tables may share a primary key. Test by attempting to insert a second child for the same parent; the database should reject it if one-to-one is mandatory.
Many-to-many usually needs a junction table
Many-to-many relationships are a common conversion checkpoint. A junction table should reference both parents and usually enforce uniqueness for the pair. It may also contain relationship-specific data such as quantity, role, start date or ordering.
Review the junction key. A composite primary key can prevent duplicate pairs, while a surrogate ID may still require a separate unique constraint on the parent-key combination if duplicates are not allowed.
Optionality becomes nullability and participation rules
If an Order may exist without an assigned SalesRep, its sales_rep_id can be nullable. If every Order must have a Customer, customer_id should normally be non-null. Be careful: not every conceptual mandatory relationship can be enforced with a simple foreign key from one side, especially when circular or minimum-count requirements are involved.
Validate what the database can enforce directly and document rules that live in application logic or deferred constraints.
Data types should preserve the domain
Keep it as text if arithmetic is meaningless or leading zeros matter.
Choose fixed precision appropriate to the currency and range.
Do not choose a short varchar merely because the ERD label is short.
Model whether time-of-day and timezone semantics actually matter.
Use a representation that matches the target engine and future domain changes.
Test constraints with records that should fail
A powerful validation technique is negative testing. Insert a child row referencing a nonexistent parent; the foreign key should reject it. Insert a duplicate value into a unique column; uniqueness should fail. Insert a null into a mandatory column; the database should reject it. Try a second one-to-one child and a duplicate junction pair.
These tests verify the database you actually created, not the SQL you think you created. They can be automated in migration tests so later schema changes do not weaken constraints accidentally.
Review delete behavior with real scenarios
Suppose a customer is deleted. Should historical orders disappear, block deletion, or become detached? There is no universal answer. Financial and audit records often need preservation, while dependent temporary data may legitimately cascade. The diagram may not show referential actions, so this is a design decision that requires domain input.
Check indexes separately from constraints
Primary and unique constraints often create indexes, but foreign keys and common query filters may need additional indexing depending on the engine and workload. Performance design should follow observed queries rather than being invented solely from an ERD image. Keep correctness and optimization as related but separate review passes.
Finish with a schema walkthrough
Read the model aloud using the created tables and constraints. “An order has one customer; an order has many order items; each item refers to one product.” If the database cannot enforce or at least represent those sentences, return to the schema. This semantic walkthrough catches problems that syntax checks cannot.
Privacy and responsible handling
LoveOCR states that uploaded and generated files are transferred securely and automatically removed from its servers within three hours. That reduces temporary server retention, but it does not replace your own data-handling responsibilities. Only process material you are authorized to use, avoid exposing secrets or personal information unnecessarily, and store downloaded results according to the rules that apply to your project or organization.
For code, database definitions, structured data, and machine-readable exports, treat generated output as a starting point that still needs human review. A file can be syntactically valid while being semantically wrong. Compare important names, identifiers, numbers, relationships, URLs, and business facts with the source before you execute, publish, import, or automate anything.
Related LoveOCR resources
Frequently asked questions
Why test invalid inserts?
They prove constraints reject states the model says are impossible, which is stronger evidence than simply seeing the constraint text.
Does a foreign key create a one-to-one relationship?
Not by itself. One-to-one usually needs uniqueness on the referencing key or another design that prevents multiple children.
How do I model many-to-many?
Usually with a junction table containing foreign keys to both parent tables and an appropriate uniqueness or primary-key rule.
Should every foreign key be NOT NULL?
No. Nullability depends on whether the relationship is optional or mandatory in the domain.
Can an ERD determine all indexes?
It can suggest important keys, but performance indexes should also be based on real query patterns and the target database engine.
Editorial note: This guide is based on the documented behavior of LoveOCR’s Image to Database ERD tool and focuses on validation, limitations, and practical downstream use instead of promising perfect output.
Updated: August 29, 2026 · Published by LoveOCR.
Validate the relationships, not just the SQL
Generate the schema from your ERD, then prove keys, optionality and cardinality with positive and negative tests in a development database.
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